{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 5: NLP on Financial Statements\n",
    "## Instructions\n",
    "Each problem consists of a function to implement and instructions on how to implement the function.  The parts of the function that need to be implemented are marked with a `# TODO` comment. After implementing the function, run the cell to test it against the unit tests we've provided. For each problem, we provide one or more unit tests from our `project_tests` package. These unit tests won't tell you if your answer is correct, but will warn you of any major errors. Your code will be checked for the correct solution when you submit it to Udacity.\n",
    "\n",
    "## Packages\n",
    "When you implement the functions, you'll only need to you use the packages you've used in the classroom, like [Pandas](https://pandas.pydata.org/) and [Numpy](http://www.numpy.org/). These packages will be imported for you. We recommend you don't add any import statements, otherwise the grader might not be able to run your code.\n",
    "\n",
    "The other packages that we're importing are `project_helper` and `project_tests`. These are custom packages built to help you solve the problems.  The `project_helper` module contains utility functions and graph functions. The `project_tests` contains the unit tests for all the problems.\n",
    "\n",
    "### Install Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import pprint\n",
    "import project_helper\n",
    "import project_tests\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download NLP Corpora\n",
    "You'll need two corpora to run this project: the stopwords corpus for removing stopwords and wordnet for lemmatizing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get 10ks\n",
    "We'll be running NLP analysis on 10-k documents. To do that, we first need to download the documents. For this project, we'll download 10-ks for a few companies. To lookup documents for these companies, we'll use their CIK. If you would like to run this against other stocks, we've provided the dict `additional_cik` for more stocks. However, the more stocks you try, the long it will take to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "cik_lookup = {\n",
    "    'AMZN': '0001018724',\n",
    "    'BMY': '0000014272',   \n",
    "    'CNP': '0001130310',\n",
    "    'CVX': '0000093410',\n",
    "    'FL': '0000850209',\n",
    "    'FRT': '0000034903',\n",
    "    'HON': '0000773840'}\n",
    "\n",
    "additional_cik = {\n",
    "    'AEP': '0000004904',\n",
    "    'AXP': '0000004962',\n",
    "    'BA': '0000012927', \n",
    "    'BK': '0001390777',\n",
    "    'CAT': '0000018230',\n",
    "    'DE': '0000315189',\n",
    "    'DIS': '0001001039',\n",
    "    'DTE': '0000936340',\n",
    "    'ED': '0001047862',\n",
    "    'EMR': '0000032604',\n",
    "    'ETN': '0001551182',\n",
    "    'GE': '0000040545',\n",
    "    'IBM': '0000051143',\n",
    "    'IP': '0000051434',\n",
    "    'JNJ': '0000200406',\n",
    "    'KO': '0000021344',\n",
    "    'LLY': '0000059478',\n",
    "    'MCD': '0000063908',\n",
    "    'MO': '0000764180',\n",
    "    'MRK': '0000310158',\n",
    "    'MRO': '0000101778',\n",
    "    'PCG': '0001004980',\n",
    "    'PEP': '0000077476',\n",
    "    'PFE': '0000078003',\n",
    "    'PG': '0000080424',\n",
    "    'PNR': '0000077360',\n",
    "    'SYY': '0000096021',\n",
    "    'TXN': '0000097476',\n",
    "    'UTX': '0000101829',\n",
    "    'WFC': '0000072971',\n",
    "    'WMT': '0000104169',\n",
    "    'WY': '0000106535',\n",
    "    'XOM': '0000034088'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get list of 10-ks\n",
    "The SEC has a limit on the number of calls you can make to the website per second. In order to avoid hiding that limit, we've created the `SecAPI` class. This will cache data from the SEC and prevent you from going over the limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec_api = project_helper.SecAPI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the class constructed, let's pull a list of filled 10-ks from the SEC for each company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def get_sec_data(cik, doc_type, start=0, count=60):\n",
    "    newest_pricing_data = pd.to_datetime('2018-01-01')\n",
    "    rss_url = 'https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany' \\\n",
    "        '&CIK={}&type={}&start={}&count={}&owner=exclude&output=atom' \\\n",
    "        .format(cik, doc_type, start, count)\n",
    "    sec_data = sec_api.get(rss_url)\n",
    "    feed = BeautifulSoup(sec_data.encode('ascii'), 'xml').feed\n",
    "    entries = [\n",
    "        (\n",
    "            entry.content.find('filing-href').getText(),\n",
    "            entry.content.find('filing-type').getText(),\n",
    "            entry.content.find('filing-date').getText())\n",
    "        for entry in feed.find_all('entry', recursive=False)\n",
    "        if pd.to_datetime(entry.content.find('filing-date').getText()) <= newest_pricing_data]\n",
    "\n",
    "    return entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pull the list using the `get_sec_data` function, then display some of the results. For displaying some of the data, we'll use Amazon as an example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('https://www.sec.gov/Archives/edgar/data/1018724/000101872417000011/0001018724-17-000011-index.htm',\n",
      "  '10-K',\n",
      "  '2017-02-10'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872416000172/0001018724-16-000172-index.htm',\n",
      "  '10-K',\n",
      "  '2016-01-29'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872415000006/0001018724-15-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2015-01-30'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872414000006/0001018724-14-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2014-01-31'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000119312513028520/0001193125-13-028520-index.htm',\n",
      "  '10-K',\n",
      "  '2013-01-30')]\n"
     ]
    }
   ],
   "source": [
    "example_ticker = 'AMZN'\n",
    "sec_data = {}\n",
    "\n",
    "for ticker, cik in cik_lookup.items():\n",
    "    sec_data[ticker] = get_sec_data(cik, '10-K')\n",
    "\n",
    "pprint.pprint(sec_data[example_ticker][:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download 10-ks\n",
    "As you see, this is a list of urls. These urls point to a file that contains metadata related to each filling. Since we don't care about the metadata, we'll pull the filling by replacing the url with the filling url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading AMZN Fillings: 100%|██████████| 22/22 [00:06<00:00,  3.42filling/s]\n",
      "Downloading BMY Fillings: 100%|██████████| 27/27 [00:12<00:00,  2.24filling/s]\n",
      "Downloading CNP Fillings: 100%|██████████| 19/19 [00:07<00:00,  2.38filling/s]\n",
      "Downloading CVX Fillings: 100%|██████████| 25/25 [00:19<00:00,  1.31filling/s]\n",
      "Downloading FL Fillings: 100%|██████████| 22/22 [00:05<00:00,  3.96filling/s]\n",
      "Downloading FRT Fillings: 100%|██████████| 29/29 [00:05<00:00,  5.29filling/s]\n",
      "Downloading HON Fillings: 100%|██████████| 25/25 [00:06<00:00,  3.94filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Document:\n",
      "\n",
      "<SEC-DOCUMENT>0001018724-17-000011.txt : 20170210\n",
      "<SEC-HEADER>0001018724-17-000011.hdr.sgml : 20170210\n",
      "<ACCEPTANCE-DATETIME>20170209175636\n",
      "ACCESSION NUMBER:\t\t0001018724-17-000011\n",
      "CONFORMED SUBMISSION TYPE:\t10-K\n",
      "PUBLIC DOCUMENT COUNT:\t\t92\n",
      "CONFORMED PERIOD OF REPORT:\t20161231\n",
      "FILED AS OF DATE:\t\t20170210\n",
      "DATE AS OF CHANGE:\t\t20170209\n",
      "\n",
      "FILER:\n",
      "\n",
      "\tCOMPANY DATA:\t\n",
      "\t\tCOMPANY CONFORMED NAME:\t\t\tAMAZON COM INC\n",
      "\t\tCENTRAL INDEX KEY:\t\t\t0001018724\n",
      "\t\tSTANDARD INDUSTRIAL CLASSIFICATION:\tRETAIL-CATALOG & MAIL-ORDER HOUSES [5961]\n",
      "\t\tIRS NUMBER:\t\t\t\t911646860\n",
      "\t\tSTATE OF INCORPORATION:\t\t\tDE\n",
      "\t\tFISCAL YEAR END:\t\t\t1231\n",
      "\n",
      "\tFILING VALUES:\n",
      "\t\tFORM TYPE:\t\t10-K\n",
      "\t\tSEC ACT:\t\t1934 Act\n",
      "\t\tSEC FILE NUMBER:\t000-22513\n",
      "\t\tFILM NUMBER:\t\t17588807\n",
      "\n",
      "\tBUSINESS ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "\t\tBUSINESS PHONE:\t\t2062661000\n",
      "\n",
      "\tMAIL ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "</SEC-HEADER>\n",
      "<DOCUMENT>\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "raw_fillings_by_ticker = {}\n",
    "\n",
    "for ticker, data in sec_data.items():\n",
    "    raw_fillings_by_ticker[ticker] = {}\n",
    "    for index_url, file_type, file_date in tqdm(data, desc='Downloading {} Fillings'.format(ticker), unit='filling'):\n",
    "        if (file_type == '10-K'):\n",
    "            file_url = index_url.replace('-index.htm', '.txt').replace('.txtl', '.txt')            \n",
    "            \n",
    "            raw_fillings_by_ticker[ticker][file_date] = sec_api.get(file_url)\n",
    "\n",
    "\n",
    "print('Example Document:\\n\\n{}...'.format(next(iter(raw_fillings_by_ticker[example_ticker].values()))[:1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Documents\n",
    "With theses fillings downloaded, we want to break them into their associated documents. These documents are sectioned off in the fillings with the tags `<DOCUMENT>` for the start of each document and `</DOCUMENT>` for the end of each document. There's no overlap with these documents, so each `</DOCUMENT>` tag should come after the `<DOCUMENT>` with no `<DOCUMENT>` tag in between.\n",
    "\n",
    "Implement `get_documents` to return a list of these documents from a filling. Make sure not to include the tag in the returned document text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "def get_documents(text):\n",
    "    \"\"\"\n",
    "    Extract the documents from the text\n",
    "    Parameters\n",
    "    ----------\n",
    "    text : str\n",
    "        The text with the document strings inside\n",
    "    Returns\n",
    "    -------\n",
    "    extracted_docs : list of str\n",
    "        The document strings found in `text`\n",
    "    \"\"\"\n",
    "    start_pattern = re.compile(r'<DOCUMENT>')\n",
    "    end_pattern = re.compile(r'</DOCUMENT>')\n",
    "\n",
    "    start_i = [i.end() for i in start_pattern.finditer(text)]\n",
    "    end_i = [i.start() for i in end_pattern.finditer(text)]\n",
    "    \n",
    "#     print(start_pattern)\n",
    "#     print(end_pattern)\n",
    "#     print(start_i)\n",
    "#     print(end_i)\n",
    "    extract= []\n",
    "    for s, e in zip(start_i, end_i):\n",
    "        extract.append(text[s:e])\n",
    "    return extract\n",
    "    \n",
    "    \n",
    "#     return extracted_docs\n",
    "\n",
    "\n",
    "project_tests.test_get_documents(get_documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_documents` function implemented, let's extract all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting Documents from AMZN Fillings: 100%|██████████| 17/17 [00:00<00:00, 27.29filling/s]\n",
      "Getting Documents from BMY Fillings: 100%|██████████| 23/23 [00:01<00:00, 12.66filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 15/15 [00:00<00:00, 18.99filling/s]\n",
      "Getting Documents from CVX Fillings: 100%|██████████| 21/21 [00:00<00:00, 35.34filling/s]\n",
      "Getting Documents from FL Fillings: 100%|██████████| 16/16 [00:00<00:00, 27.96filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 19/19 [00:02<00:00,  8.87filling/s]\n",
      "Getting Documents from HON Fillings: 100%|██████████| 20/20 [00:00<00:00, 25.88filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document 0 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>amzn-20161231x10k.htm\n",
      "<DESCRIPTION>FORM 10-K\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
      "<html>\n",
      "\t<he...\n",
      "\n",
      "Document 1 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-12.1\n",
      "<SEQUENCE>2\n",
      "<FILENAME>amzn-20161231xex121.htm\n",
      "<DESCRIPTION>COMPUTATION OF RATIO OF EARNINGS TO FIXED CHARGES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http:...\n",
      "\n",
      "Document 2 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-21.1\n",
      "<SEQUENCE>3\n",
      "<FILENAME>amzn-20161231xex211.htm\n",
      "<DESCRIPTION>LIST OF SIGNIFICANT SUBSIDIARIES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/h...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filling_documents_by_ticker = {}\n",
    "\n",
    "for ticker, raw_fillings in raw_fillings_by_ticker.items():\n",
    "    filling_documents_by_ticker[ticker] = {}\n",
    "    for file_date, filling in tqdm(raw_fillings.items(), desc='Getting Documents from {} Fillings'.format(ticker), unit='filling'):\n",
    "        filling_documents_by_ticker[ticker][file_date] = get_documents(filling)\n",
    "\n",
    "\n",
    "print('\\n\\n'.join([\n",
    "    'Document {} Filed on {}:\\n{}...'.format(doc_i, file_date, doc[:200])\n",
    "    for file_date, docs in filling_documents_by_ticker[example_ticker].items()\n",
    "    for doc_i, doc in enumerate(docs)][:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Document Types\n",
    "Now that we have all the documents, we want to find the 10-k form in this 10-k filing. Implement the `get_document_type` function to return the type of document given. The document type is located on a line with the `<TYPE>` tag. For example, a form of type \"TEST\" would have the line `<TYPE>TEST`. Make sure to return the type as lowercase, so this example would be returned as \"test\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def get_document_type(doc):\n",
    "    \"\"\"\n",
    "    Return the document type lowercased\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    doc : str\n",
    "        The document string\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    doc_type : str\n",
    "        The document type lowercased\n",
    "    \"\"\"\n",
    "    pattern = re.compile(r'\\<TYPE>[^\\n]+')\n",
    "    \n",
    "    doc_type = pattern.findall(doc)[0][len('<TYPE>'):]\n",
    "#     print(doc)\n",
    "#     print(doc_type.lower())\n",
    "    return doc_type.lower()\n",
    "\n",
    "#     doc_type = [i[len('<TYPE>'):] for i in pattern.findall(doc)]\n",
    "#     return doc_type[0].lower()\n",
    "    \n",
    "project_tests.test_get_document_type(get_document_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_document_type` function, we'll filter out all non 10-k documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2016123...\n",
      "    file_date: '2017-02-10'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2015123...\n",
      "    file_date: '2016-01-29'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2014123...\n",
      "    file_date: '2015-01-30'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2013123...\n",
      "    file_date: '2014-01-31'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>d445434d10k....\n",
      "    file_date: '2013-01-30'},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "ten_ks_by_ticker = {}\n",
    "\n",
    "for ticker, filling_documents in filling_documents_by_ticker.items():\n",
    "    ten_ks_by_ticker[ticker] = []\n",
    "    for file_date, documents in filling_documents.items():\n",
    "        for document in documents:\n",
    "            if get_document_type(document) == '10-k':\n",
    "                ten_ks_by_ticker[ticker].append({\n",
    "                    'cik': cik_lookup[ticker],\n",
    "                    'file': document,\n",
    "                    'file_date': file_date})\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['cik', 'file', 'file_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "### Clean Up\n",
    "As you can see, the text for the documents are very messy. To clean this up, we'll remove the html and lowercase all the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_html_tags(text):\n",
    "    text = BeautifulSoup(text, 'html.parser').get_text()\n",
    "    \n",
    "    return text\n",
    "\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = remove_html_tags(text)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `clean_text` function, we'll clean up all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cleaning AMZN 10-Ks: 100%|██████████| 17/17 [00:34<00:00,  2.05s/10-K]\n",
      "Cleaning BMY 10-Ks: 100%|██████████| 23/23 [01:13<00:00,  3.20s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 15/15 [00:54<00:00,  3.63s/10-K]\n",
      "Cleaning CVX 10-Ks: 100%|██████████| 21/21 [01:54<00:00,  5.44s/10-K]\n",
      "Cleaning FL 10-Ks: 100%|██████████| 16/16 [00:26<00:00,  1.66s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 19/19 [00:53<00:00,  2.83s/10-K]\n",
      "Cleaning HON 10-Ks: 100%|██████████| 20/20 [00:58<00:00,  2.94s/10-K]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20161231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20151231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20141231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20131231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nd445434d10k.htm\\nform 10-k\\n\\n\\nform 1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Cleaning {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_clean'] = clean_text(ten_k['file'])\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_clean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatize\n",
    "With the text cleaned up, it's time to distill the verbs down. Implement the `lemmatize_words` function to lemmatize verbs in the list of words provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "\n",
    "def lemmatize_words(words):\n",
    "    \"\"\"\n",
    "    Lemmatize words \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    words : list of str\n",
    "        List of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    lemmatized_words : list of str\n",
    "        List of lemmatized words\n",
    "    \"\"\"\n",
    "#     print(words)\n",
    "    lem_words = [WordNetLemmatizer().lemmatize(i,'v') for i in words]\n",
    "#     print(lem_words)\n",
    "    return lem_words\n",
    "\n",
    "\n",
    "project_tests.test_lemmatize_words(lemmatize_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `lemmatize_words` function implemented, let's lemmatize all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lemmatize AMZN 10-Ks: 100%|██████████| 17/17 [00:04<00:00,  3.4210-K/s]\n",
      "Lemmatize BMY 10-Ks: 100%|██████████| 23/23 [00:10<00:00,  2.1110-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 15/15 [00:08<00:00,  1.6710-K/s]\n",
      "Lemmatize CVX 10-Ks: 100%|██████████| 21/21 [00:10<00:00,  1.9910-K/s]\n",
      "Lemmatize FL 10-Ks: 100%|██████████| 16/16 [00:04<00:00,  3.8610-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 19/19 [00:06<00:00,  2.8610-K/s]\n",
      "Lemmatize HON 10-Ks: 100%|██████████| 20/20 [00:06<00:00,  3.1110-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20161231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20151231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20141231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20131231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'd445434d10k', 'htm', 'form', '1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word_pattern = re.compile('\\w+')\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Lemmatize {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_lemma'] = lemmatize_words(word_pattern.findall(ten_k['file_clean']))\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_lemma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Remove Stop Words for AMZN 10-Ks: 100%|██████████| 17/17 [00:02<00:00,  7.9510-K/s]\n",
      "Remove Stop Words for BMY 10-Ks: 100%|██████████| 23/23 [00:04<00:00,  4.9510-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 15/15 [00:03<00:00,  3.7810-K/s]\n",
      "Remove Stop Words for CVX 10-Ks: 100%|██████████| 21/21 [00:04<00:00,  4.6110-K/s]\n",
      "Remove Stop Words for FL 10-Ks: 100%|██████████| 16/16 [00:01<00:00,  8.7910-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  6.4610-K/s]\n",
      "Remove Stop Words for HON 10-Ks: 100%|██████████| 20/20 [00:02<00:00,  7.5010-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stop Words Removed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "\n",
    "lemma_english_stopwords = lemmatize_words(stopwords.words('english'))\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Remove Stop Words for {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_lemma'] = [word for word in ten_k['file_lemma'] if word not in lemma_english_stopwords]\n",
    "\n",
    "\n",
    "print('Stop Words Removed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis on 10ks\n",
    "### Loughran McDonald Sentiment Word Lists\n",
    "We'll be using the Loughran and McDonald sentiment word lists. These word lists cover the following sentiment:\n",
    "- Negative \n",
    "- Positive\n",
    "- Uncertainty\n",
    "- Litigious\n",
    "- Constraining\n",
    "- Superfluous\n",
    "- Modal\n",
    "\n",
    "This will allow us to do the sentiment analysis on the 10-ks. Let's first load these word lists. We'll be looking into a few of these sentiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>litigious</th>\n",
       "      <th>constraining</th>\n",
       "      <th>interesting</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abandon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abandonment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abandonments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abdicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>abdication</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    negative  positive  uncertainty  litigious  constraining  interesting  \\\n",
       "9       True     False        False      False         False        False   \n",
       "12      True     False        False      False         False        False   \n",
       "13      True     False        False      False         False        False   \n",
       "51      True     False        False      False         False        False   \n",
       "54      True     False        False      False         False        False   \n",
       "\n",
       "            word  \n",
       "9        abandon  \n",
       "12   abandonment  \n",
       "13  abandonments  \n",
       "51      abdicate  \n",
       "54    abdication  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "sentiments = ['negative', 'positive', 'uncertainty', 'litigious', 'constraining', 'interesting']\n",
    "\n",
    "sentiment_df = pd.read_csv(os.path.join('..', '..', 'data', 'project_5_loughran_mcdonald', 'loughran_mcdonald_master_dic_2016.csv'))\n",
    "sentiment_df.columns = [column.lower() for column in sentiment_df.columns] # Lowercase the columns for ease of use\n",
    "\n",
    "# Remove unused information\n",
    "sentiment_df = sentiment_df[sentiments + ['word']]\n",
    "sentiment_df[sentiments] = sentiment_df[sentiments].astype(bool)\n",
    "sentiment_df = sentiment_df[(sentiment_df[sentiments]).any(1)]\n",
    "\n",
    "# Apply the same preprocessing to these words as the 10-k words\n",
    "sentiment_df['word'] = lemmatize_words(sentiment_df['word'].str.lower())\n",
    "sentiment_df = sentiment_df.drop_duplicates('word')\n",
    "\n",
    "\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag of Words\n",
    "using the sentiment word lists, let's generate sentiment bag of words from the 10-k documents. Implement `get_bag_of_words` to generate a bag of words that counts the number of sentiment words in each doc. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict, Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "\n",
    "def get_bag_of_words(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate a bag of words from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "#     print(sentiment_words)\n",
    "#     print(docs)\n",
    "    cv = CountVectorizer(vocabulary=sentiment_words)\n",
    "#     print(cv)\n",
    "    bag_words = cv.fit_transform(docs).toarray()\n",
    "#     print(bag_words)\n",
    "    \n",
    "    return bag_words\n",
    "\n",
    "\n",
    "project_tests.test_get_bag_of_words(get_bag_of_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_bag_of_words` function, we'll generate a bag of words for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    positive: '[[16  0  0 ...,  0  0  0]\\n [16  0  0 ...,  0  0 ...\n",
      "    uncertainty: '[[0 0 0 ..., 1 1 3]\\n [0 0 0 ..., 1 1 3]\\n [0 0 0...\n",
      "    litigious: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    constraining: '[[0 0 0 ..., 0 0 2]\\n [0 0 0 ..., 0 0 2]\\n [0 0 0...\n",
      "    interesting: '[[2 0 0 ..., 0 0 0]\\n [2 0 0 ..., 0 0 0]\\n [2 0 0...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_bow_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['file_lemma']) for ten_k in ten_ks]\n",
    "    \n",
    "    sentiment_bow_ten_ks[ticker] = {\n",
    "        sentiment: get_bag_of_words(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data([sentiment_bow_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard Similarity\n",
    "Using the bag of words, let's calculate the jaccard similarity on the bag of words and plot it over time. Implement `get_jaccard_similarity` to return the jaccard similarities between each tick in time. Since the input, `bag_of_words_matrix`, is a bag of words for each time period in order, you just need to compute the jaccard similarities for each neighboring bag of words. Make sure to turn the bag of words into a boolean array when calculating the jaccard similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 1 0 0 0 1]\n",
      " [0 1 2 0 1 1 1]\n",
      " [1 0 0 1 0 0 0]]\n",
      "[[False  True  True False False False  True]\n",
      " [False  True  True False  True  True  True]\n",
      " [ True False False  True False False False]]\n",
      "[0.7142857142857143, 0.0]\n",
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "\n",
    "\n",
    "def get_jaccard_similarity(bag_of_words_matrix):\n",
    "    \"\"\"\n",
    "    Get jaccard similarities for neighboring documents\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    jaccard_similarities : list of float\n",
    "        Jaccard similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    print(bag_of_words_matrix)\n",
    "    bag_words = bag_of_words_matrix.astype(bool)\n",
    "    print(bag_words)\n",
    "    jac = [jaccard_similarity_score(i,j) for i,j in zip(bag_words, bag_words[1:])]\n",
    "    print(jac)\n",
    "    \n",
    "    return jac\n",
    "\n",
    "\n",
    "project_tests.test_get_jaccard_similarity(get_jaccard_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_jaccard_similarity` function, let's plot the similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 2 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ...,  True False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.99471947194719468, 0.99339933993399343, 0.99207920792079207, 0.98811881188118811, 0.98943894389438947, 0.98877887788778873, 0.99075907590759071, 0.98877887788778873, 0.99009900990099009, 0.99405940594059405, 0.98415841584158414, 0.99009900990099009, 0.98283828382838279, 0.9808580858085808, 0.96039603960396036, 0.95841584158415838]\n",
      "[[16  0  0 ...,  0  0  0]\n",
      " [16  0  0 ...,  0  0  0]\n",
      " [16  0  0 ...,  0  0  0]\n",
      " ..., \n",
      " [19  0  0 ...,  0  0  0]\n",
      " [17  0  0 ...,  1  0  0]\n",
      " [12  0  0 ...,  0  0  0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " ..., \n",
      " [ True False False ..., False False False]\n",
      " [ True False False ...,  True False False]\n",
      " [ True False False ..., False False False]]\n",
      "[1.0, 0.99598393574297184, 0.99196787148594379, 0.98393574297188757, 0.98795180722891562, 0.99598393574297184, 0.98393574297188757, 0.98795180722891562, 0.98393574297188757, 0.99598393574297184, 0.96385542168674698, 0.99196787148594379, 0.98393574297188757, 0.96385542168674698, 0.91566265060240959, 0.91967871485943775]\n",
      "[[0 0 0 ..., 1 1 3]\n",
      " [0 0 0 ..., 1 1 3]\n",
      " [0 0 0 ..., 2 1 3]\n",
      " ..., \n",
      " [0 0 1 ..., 1 0 5]\n",
      " [0 0 0 ..., 1 0 3]\n",
      " [0 0 0 ..., 1 0 3]]\n",
      "[[False False False ...,  True  True  True]\n",
      " [False False False ...,  True  True  True]\n",
      " [False False False ...,  True  True  True]\n",
      " ..., \n",
      " [False False  True ...,  True False  True]\n",
      " [False False False ...,  True False  True]\n",
      " [False False False ...,  True False  True]]\n",
      "[0.9956521739130435, 0.9869565217391304, 0.99130434782608701, 0.9956521739130435, 0.9956521739130435, 0.97391304347826091, 0.9956521739130435, 0.9869565217391304, 0.9869565217391304, 0.9956521739130435, 0.9652173913043478, 0.9652173913043478, 0.95652173913043481, 0.95217391304347831, 0.89130434782608692, 0.94347826086956521]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.99301675977653636, 0.9972067039106145, 0.99301675977653636, 0.98882681564245811, 0.98463687150837986, 0.97905027932960897, 0.98743016759776536, 0.98882681564245811, 0.99162011173184361, 0.99162011173184361, 0.99022346368715086, 0.99022346368715086, 0.97625698324022347, 0.99301675977653636, 0.95949720670391059, 0.96089385474860334]\n",
      "[[0 0 0 ..., 0 0 2]\n",
      " [0 0 0 ..., 0 0 2]\n",
      " [0 0 0 ..., 0 0 2]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 2]\n",
      " [0 2 0 ..., 0 0 2]\n",
      " [0 0 0 ..., 0 0 1]]\n",
      "[[False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " ..., \n",
      " [False False False ..., False False  True]\n",
      " [False  True False ..., False False  True]\n",
      " [False False False ..., False False  True]]\n",
      "[0.98958333333333337, 0.98958333333333337, 0.96875, 0.95833333333333337, 0.97916666666666663, 0.97916666666666663, 1.0, 0.94791666666666663, 0.98958333333333337, 0.97916666666666663, 0.9375, 1.0, 0.94791666666666663, 0.94791666666666663, 0.86458333333333337, 0.88541666666666663]\n",
      "[[2 0 0 ..., 0 0 0]\n",
      " [2 0 0 ..., 0 0 0]\n",
      " [2 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [1 0 0 ..., 0 0 3]\n",
      " [3 0 0 ..., 0 0 0]\n",
      " [4 0 0 ..., 0 0 0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " ..., \n",
      " [ True False False ..., False False  True]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]]\n",
      "[0.98333333333333328, 0.98333333333333328, 1.0, 0.96666666666666667, 1.0, 0.98333333333333328, 1.0, 1.0, 0.96666666666666667, 0.98333333333333328, 0.94999999999999996, 0.98333333333333328, 0.98333333333333328, 0.98333333333333328, 0.94999999999999996, 0.93333333333333335]\n",
      "[[0 0 0 ..., 2 0 0]\n",
      " [0 0 0 ..., 1 0 0]\n",
      " [0 2 0 ..., 1 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ...,  True False False]\n",
      " [False False False ...,  True False False]\n",
      " [False  True False ...,  True False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.9782178217821782, 0.98283828382838279, 0.98481848184818477, 0.98415841584158414, 0.97557755775577559, 0.97425742574257423, 0.97491749174917497, 0.97161716171617163, 0.97095709570957101, 0.95775577557755776, 0.97029702970297027, 0.9610561056105611, 0.95379537953795379, 0.93597359735973595, 0.93795379537953794, 0.98151815181518154, 0.97953795379537956, 0.97293729372937299, 0.98481848184818477, 0.98151815181518154, 0.97491749174917497, 0.95115511551155119]\n",
      "[[7 0 0 ..., 0 0 0]\n",
      " [7 0 0 ..., 0 0 0]\n",
      " [7 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.95983935742971882, 0.97590361445783136, 0.98393574297188757, 0.94779116465863456, 0.97590361445783136, 0.96787148594377514, 0.96385542168674698, 0.94779116465863456, 0.9718875502008032, 0.96385542168674698, 0.9718875502008032, 0.9718875502008032, 0.95180722891566261, 0.85943775100401609, 0.91566265060240959, 0.95983935742971882, 0.94377510040160639, 0.96787148594377514, 0.94779116465863456, 0.95582329317269077, 0.93172690763052213, 0.91967871485943775]\n",
      "[[0 0 0 ..., 0 0 3]\n",
      " [0 0 0 ..., 0 0 4]\n",
      " [0 0 0 ..., 0 0 6]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 1]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " ..., \n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.97826086956521741, 0.97391304347826091, 0.9826086956521739, 0.9652173913043478, 0.95652173913043481, 0.95652173913043481, 0.94347826086956521, 0.92608695652173911, 0.94782608695652171, 0.95217391304347831, 0.97391304347826091, 0.94782608695652171, 0.93478260869565222, 0.93043478260869561, 0.92608695652173911, 0.9826086956521739, 0.96086956521739131, 0.95217391304347831, 0.9826086956521739, 0.97391304347826091, 0.95652173913043481, 0.88695652173913042]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 1 0]\n",
      " [0 0 0 ..., 0 3 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False  True False]\n",
      " [False False False ..., False  True False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.97206703910614523, 0.98743016759776536, 0.97765363128491622, 0.98463687150837986, 0.99022346368715086, 0.98603351955307261, 0.98044692737430172, 0.97486033519553073, 0.98603351955307261, 0.97905027932960897, 0.97486033519553073, 0.97625698324022347, 0.97206703910614523, 0.95530726256983245, 0.96368715083798884, 0.98743016759776536, 0.98044692737430172, 0.99022346368715086, 0.994413407821229, 0.98184357541899436, 0.97625698324022347, 0.91759776536312854]\n",
      "[[0 2 0 ..., 1 0 0]\n",
      " [0 4 0 ..., 1 0 0]\n",
      " [0 6 0 ..., 1 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 3 0 ..., 0 0 1]]\n",
      "[[False  True False ...,  True False False]\n",
      " [False  True False ...,  True False False]\n",
      " [False  True False ...,  True False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False  True False ..., False False  True]]\n",
      "[0.98958333333333337, 0.97916666666666663, 0.96875, 0.97916666666666663, 1.0, 0.97916666666666663, 0.94791666666666663, 0.95833333333333337, 0.95833333333333337, 0.96875, 0.94791666666666663, 0.94791666666666663, 0.98958333333333337, 0.82291666666666663, 0.89583333333333337, 0.98958333333333337, 0.97916666666666663, 0.98958333333333337, 1.0, 0.97916666666666663, 0.96875, 0.80208333333333337]\n",
      "[[1 1 0 ..., 0 6 0]\n",
      " [2 1 0 ..., 0 8 0]\n",
      " [1 1 0 ..., 0 7 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 2 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[ True  True False ..., False  True False]\n",
      " [ True  True False ..., False  True False]\n",
      " [ True  True False ..., False  True False]\n",
      " ..., \n",
      " [False False False ..., False  True False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.94999999999999996, 0.93333333333333335, 0.94999999999999996, 0.98333333333333328, 0.98333333333333328, 0.98333333333333328, 0.94999999999999996, 0.94999999999999996, 0.96666666666666667, 0.96666666666666667, 0.98333333333333328, 0.93333333333333335, 0.96666666666666667, 0.80000000000000004, 0.84999999999999998, 0.98333333333333328, 0.96666666666666667, 0.96666666666666667, 0.94999999999999996, 0.96666666666666667, 0.94999999999999996, 0.93333333333333335]\n",
      "[[1 1 0 ..., 0 0 0]\n",
      " [1 1 0 ..., 0 0 0]\n",
      " [1 1 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 1 0 ..., 0 0 0]\n",
      " [0 1 0 ..., 0 0 0]\n",
      " [0 1 0 ..., 0 0 0]]\n",
      "[[ True  True False ..., False False False]\n",
      " [ True  True False ..., False False False]\n",
      " [ True  True False ..., False False False]\n",
      " ..., \n",
      " [False  True False ..., False False False]\n",
      " [False  True False ..., False False False]\n",
      " [False  True False ..., False False False]]\n",
      "[0.9782178217821782, 0.97953795379537956, 0.9808580858085808, 0.96039603960396036, 0.98283828382838279, 0.97491749174917497, 0.98415841584158414, 0.98151815181518154, 0.97095709570957101, 0.98613861386138613, 0.96831683168316829, 0.95973597359735974, 0.96435643564356432, 0.96171617161716172]\n",
      "[[17  0  0 ...,  0  0  0]\n",
      " [17  0  0 ...,  0  0  0]\n",
      " [17  0  0 ...,  0  0  0]\n",
      " ..., \n",
      " [10  0  0 ...,  0  0  0]\n",
      " [15  0  0 ...,  0  0  0]\n",
      " [27  0  0 ...,  0  0  0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " ..., \n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]]\n",
      "[0.9718875502008032, 0.97590361445783136, 0.95983935742971882, 0.95180722891566261, 0.98393574297188757, 0.96787148594377514, 1.0, 0.96787148594377514, 0.94779116465863456, 0.9718875502008032, 0.95180722891566261, 0.94779116465863456, 0.94377510040160639, 0.96787148594377514]\n",
      "[[ 0  0  1 ...,  4  5 20]\n",
      " [ 0  0  2 ...,  3  3 13]\n",
      " [ 0  0  2 ...,  3  3  8]\n",
      " ..., \n",
      " [ 0  0  1 ...,  0  1  7]\n",
      " [ 0  0  1 ...,  1  1  4]\n",
      " [ 0  0  1 ...,  2  1  5]]\n",
      "[[False False  True ...,  True  True  True]\n",
      " [False False  True ...,  True  True  True]\n",
      " [False False  True ...,  True  True  True]\n",
      " ..., \n",
      " [False False  True ..., False  True  True]\n",
      " [False False  True ...,  True  True  True]\n",
      " [False False  True ...,  True  True  True]]\n",
      "[0.9695652173913043, 0.97826086956521741, 0.9869565217391304, 0.96086956521739131, 0.9695652173913043, 0.9695652173913043, 0.97826086956521741, 0.96086956521739131, 0.96086956521739131, 0.9826086956521739, 0.95652173913043481, 0.94782608695652171, 0.93478260869565222, 0.94347826086956521]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 6 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False  True False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.98324022346368711, 0.98324022346368711, 0.98743016759776536, 0.98044692737430172, 0.99022346368715086, 0.98743016759776536, 0.98882681564245811, 0.98882681564245811, 0.98184357541899436, 0.99162011173184361, 0.98044692737430172, 0.97905027932960897, 0.95949720670391059, 0.98044692737430172]\n",
      "[[0 0 0 ..., 1 1 4]\n",
      " [0 0 0 ..., 1 1 5]\n",
      " [0 1 0 ..., 1 1 5]\n",
      " ..., \n",
      " [0 0 0 ..., 1 0 8]\n",
      " [0 0 0 ..., 0 0 3]\n",
      " [0 0 0 ..., 0 0 3]]\n",
      "[[False False False ...,  True  True  True]\n",
      " [False False False ...,  True  True  True]\n",
      " [False  True False ...,  True  True  True]\n",
      " ..., \n",
      " [False False False ...,  True False  True]\n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False  True]]\n",
      "[0.95833333333333337, 0.94791666666666663, 0.95833333333333337, 0.92708333333333337, 0.98958333333333337, 0.96875, 0.96875, 0.97916666666666663, 0.95833333333333337, 0.95833333333333337, 0.95833333333333337, 0.97916666666666663, 0.92708333333333337, 0.92708333333333337]\n",
      "[[  0   0   0   8  29   1   0   2   0   3   0   0   1   0  80   0   0   0\n",
      "    0   0   0   5   0   1   0   1   0   0 202   0   0   0   0   0   0   0\n",
      "   77   0   0   0   0   0   5   0   6   0   0   0   0   0   0   0   0   0\n",
      "    0   5   0   0   0   0]\n",
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      "    0   0   0   5   0   8   0   0   0   0 235   0   0   0   0   0   0   0\n",
      "   64   0   0   0   0   0   6   0   6   0   0   0   0   0   0   0   0   0\n",
      "    0   6   0   0   0   0]\n",
      " [  0   0   0  11  38   1   0   2   0   3   0   3   1   0  80   0   0   0\n",
      "    0   0   0   2   0  11   0   0   0   0 207   0   0   0   0   0   0   0\n",
      "   65   0   0   0   0   0   6   0   6   0   0   0   0   0   0   0   0   0\n",
      "    0   6   0   0   0   0]\n",
      " [  0   0   0   9  44   0   0   2   0   2   0   3   1   0  77   0   0   0\n",
      "    0   0   0   2   0  23   0   0   0   0 177   0   0   0   0   0   0   0\n",
      "   62   0   0   0   0   0   6   0   6   0   0   0   0   0   0   0   0   0\n",
      "    0   6   0   0   0   0]\n",
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      "    0   1   0   2   0  31   0   0   0   0 137   0   0   0   0   0   0   0\n",
      "   58   0   0   0   0   0   3   0   5   0   0   0   0   0   0   0   0   0\n",
      "    0   5   0   0   0   0]\n",
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      "    0   1   0   2   0  32   0   0   0   0 159   0   0   0   0   0   0   0\n",
      "   68   0   0   0   0   0   5   0   5   0   0   0   0   0   0   0   0   0\n",
      "    0   5   0   0   0   0]\n",
      " [  0   0   0   9  57   0   0   0   0   1   0   0   1   0  68   0   0   0\n",
      "    0   1   0   2   0   6   0   0   0   0 192   0   0   0   0   0   0   0\n",
      "   88   0   0   0   0   0   4   0   5   0   0   0   0   0   0   0   0   0\n",
      "    0   5   0   0   0   0]\n",
      " [  0   0   0   9  63   0   0   0   0   1   4   0   1   0  60   0   0   0\n",
      "    0   1   0   2   0  14   0   1   0   0 173   0   0   0   0   0   0   0\n",
      "   68   0   0   0   0   0   5   0   5   0   0   0   0   0   0   0   0   0\n",
      "    0   6   0   0   0   0]\n",
      " [  0   0   0   9  57   0   0   0   0   0   4   0   1   0  37   0   0   0\n",
      "    0   1   0   4   0  15   0   6   0   0 173   0   0   0   0   0   0   0\n",
      "   63   0   0   0   0   0   9   0   5   0   0   0   0   0   0   0   0   0\n",
      "    0   6   0   0   0   0]\n",
      " [  0   0   0   9  52   0   0   0   0   0   4   0   1   0  44   0   0   0\n",
      "    0   0   0   3   0  52   0   1   0   0 155   0   0   0   0   0   0   0\n",
      "   54   0   0   0   0   0  11   1   5   0   0   0   0   0   0   0   0   0\n",
      "    0   6   0   0   0   0]\n",
      " [  0   0   0   9  73   0   0   0   0   0   4   0   1   0  47   0   0   0\n",
      "    0   0   0   2   0  50   0   5   0   0 160   0   0   0   0   0   0   0\n",
      "   90   0   0   0   0   0   4   1   5   0   0   0   0   0   0   0   0   0\n",
      "    0   6   0   0   0   0]\n",
      " [  0   0   0   9  86   0   0   1   2   0   5   0   1   0  42   0   0   0\n",
      "    0   0   0   1   0  57   0   0   0   0 153   0   0   0   0   0   0   0\n",
      "   90   0   0   0   0   0   5   0   5   0   0   0   0   0   0   0   0   0\n",
      "    0   5   0   0   0   0]\n",
      " [  0   0   0   8  67   0   0   5   2   0   4   0   1   0  34   0   0   0\n",
      "    0   0   0   1   0  49   0   1   0   0 125   0   0   0   0   0   0   0\n",
      "  115   0   0   0   0   0   2   0   3   0   0   0   0   0   0   0   0   0\n",
      "    0   4   0   0   0   0]\n",
      " [  0   0   0  10  63   0   0   0   2   0   4   0   1   0  53   0   0   0\n",
      "    0   0   0   1   0  11   0   2   0   0 112   0   0   0   0   0   0   0\n",
      "   89   0   0   0   0   0  18   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   2   2   0   0   0]\n",
      " [  0   0   0   9  53   0   0   4   1   0   5   1   0   0  72   0   0   0\n",
      "    0   0   0   1   0  66   0   3   0   0 131   0   0   0   0   0   0   0\n",
      "   63   0   0   0   0   0   8   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   2   2   0   0   0]]\n",
      "[[False False False  True  True  True False  True False  True False False\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True  True False  True False  True False  True\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True  True False  True False  True False  True\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False  True False  True False  True\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False  True False  True False False\n",
      "   True False  True False False False False  True False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False  True False  True False False\n",
      "   True False  True False False False False  True False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False False False  True False False\n",
      "   True False  True False False False False  True False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False False False  True  True False\n",
      "   True False  True False False False False  True False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False False False False  True False\n",
      "   True False  True False False False False  True False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False False False False  True False\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True  True  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False False False False  True False\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True  True  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False  True  True False  True False\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False  True  True False  True False\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False  True False False False False]\n",
      " [False False False  True  True False False False  True False  True False\n",
      "   True False  True False False False False False False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False  True  True False False False]\n",
      " [False False False  True  True False False  True  True False  True  True\n",
      "  False False  True False False False False False False  True False  True\n",
      "  False  True False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False  True  True False False False]]\n",
      "[0.96666666666666667, 1.0, 0.98333333333333328, 0.96666666666666667, 1.0, 0.98333333333333328, 0.96666666666666667, 0.98333333333333328, 0.96666666666666667, 1.0, 0.93333333333333335, 0.98333333333333328, 0.94999999999999996, 0.94999999999999996]\n",
      "[[ 4  6  0 ...,  1  0  0]\n",
      " [ 4  6  0 ...,  1  0  0]\n",
      " [ 3  6  1 ...,  1  0  0]\n",
      " ..., \n",
      " [ 3 12  0 ...,  0  0  0]\n",
      " [ 3 12  0 ...,  0  0  0]\n",
      " [ 1 12  0 ...,  0  0  0]]\n",
      "[[ True  True False ...,  True False False]\n",
      " [ True  True False ...,  True False False]\n",
      " [ True  True  True ...,  True False False]\n",
      " ..., \n",
      " [ True  True False ..., False False False]\n",
      " [ True  True False ..., False False False]\n",
      " [ True  True False ..., False False False]]\n",
      "[0.98019801980198018, 0.98151815181518154, 0.98217821782178216, 0.98745874587458748, 0.98283828382838279, 0.97689768976897695, 0.97227722772277225, 0.97953795379537956, 0.97689768976897695, 0.98217821782178216, 0.97623762376237622, 0.98349834983498352, 0.97227722772277225, 0.97491749174917497, 0.94719471947194722, 0.97293729372937299, 0.94719471947194722, 0.92805280528052803, 0.96699669966996704, 0.95973597359735974]\n",
      "[[3 0 0 ..., 0 0 0]\n",
      " [2 0 0 ..., 0 0 0]\n",
      " [2 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [4 1 0 ..., 0 0 0]\n",
      " [2 0 0 ..., 0 0 0]\n",
      " [2 0 0 ..., 0 0 0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " ..., \n",
      " [ True  True False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]]\n",
      "[0.97991967871485941, 0.97991967871485941, 0.97590361445783136, 0.97991967871485941, 0.98795180722891562, 0.97590361445783136, 0.9718875502008032, 0.94377510040160639, 0.97590361445783136, 0.97991967871485941, 0.94779116465863456, 0.95180722891566261, 0.93975903614457834, 0.95983935742971882, 0.92369477911646591, 0.94779116465863456, 0.90763052208835338, 0.87951807228915657, 0.91967871485943775, 0.92369477911646591]\n",
      "[[0 0 0 ..., 1 0 7]\n",
      " [0 0 0 ..., 1 0 6]\n",
      " [0 0 0 ..., 1 0 7]\n",
      " ..., \n",
      " [0 0 2 ..., 0 0 1]\n",
      " [0 0 1 ..., 0 0 1]\n",
      " [0 0 1 ..., 0 0 0]]\n",
      "[[False False False ...,  True False  True]\n",
      " [False False False ...,  True False  True]\n",
      " [False False False ...,  True False  True]\n",
      " ..., \n",
      " [False False  True ..., False False  True]\n",
      " [False False  True ..., False False  True]\n",
      " [False False  True ..., False False False]]\n",
      "[0.9869565217391304, 0.9869565217391304, 0.9956521739130435, 0.9826086956521739, 0.9869565217391304, 0.95217391304347831, 0.9695652173913043, 0.97391304347826091, 0.9695652173913043, 0.97826086956521741, 0.95217391304347831, 0.97391304347826091, 0.95217391304347831, 0.94347826086956521, 0.91739130434782612, 0.9695652173913043, 0.95652173913043481, 0.92608695652173911, 0.95217391304347831, 0.96086956521739131]\n",
      "[[0 0 0 ..., 0 2 0]\n",
      " [0 0 0 ..., 0 1 0]\n",
      " [0 0 0 ..., 0 2 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 1 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False  True False]\n",
      " [False False False ..., False  True False]\n",
      " [False False False ..., False  True False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False  True False]\n",
      " [False False False ..., False False False]]\n",
      "[0.994413407821229, 0.99581005586592175, 0.9972067039106145, 0.99162011173184361, 0.99162011173184361, 0.98184357541899436, 0.97765363128491622, 0.98603351955307261, 0.99022346368715086, 0.99301675977653636, 0.98603351955307261, 0.99581005586592175, 0.98743016759776536, 0.98603351955307261, 0.96089385474860334, 0.98184357541899436, 0.97625698324022347, 0.96508379888268159, 0.98603351955307261, 0.97206703910614523]\n",
      "[[0 5 0 ..., 0 0 0]\n",
      " [0 7 0 ..., 0 0 0]\n",
      " [0 7 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 1 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False  True False ..., False False False]\n",
      " [False  True False ..., False False False]\n",
      " [False  True False ..., False False False]\n",
      " ..., \n",
      " [False  True False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.96875, 0.97916666666666663, 0.98958333333333337, 0.96875, 0.97916666666666663, 0.96875, 0.96875, 0.96875, 0.96875, 0.96875, 0.9375, 0.9375, 0.95833333333333337, 0.96875, 0.9375, 0.97916666666666663, 0.9375, 0.90625, 0.9375, 0.96875]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [1 1 0 ..., 0 0 0]\n",
      " [3 1 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [ True  True False ..., False False False]\n",
      " [ True  True False ..., False False False]]\n",
      "[1.0, 0.96666666666666667, 0.96666666666666667, 0.94999999999999996, 0.93333333333333335, 0.96666666666666667, 0.96666666666666667, 0.96666666666666667, 0.93333333333333335, 0.98333333333333328, 0.98333333333333328, 1.0, 0.94999999999999996, 1.0, 0.91666666666666663, 0.98333333333333328, 0.96666666666666667, 0.93333333333333335, 0.93333333333333335, 0.93333333333333335]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.98679867986798675, 0.99075907590759071, 0.99009900990099009, 0.99471947194719468, 0.99141914191419145, 0.9854785478547855, 0.98613861386138613, 0.97953795379537956, 0.97425742574257423, 0.9808580858085808, 0.97491749174917497, 0.96897689768976902, 0.9808580858085808, 0.9636963696369637, 0.99405940594059405]\n",
      "[[8 0 0 ..., 0 0 0]\n",
      " [8 0 0 ..., 0 0 0]\n",
      " [7 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [2 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " ..., \n",
      " [ True False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.98393574297188757, 0.96385542168674698, 0.97991967871485941, 0.98795180722891562, 0.96385542168674698, 0.97590361445783136, 0.96385542168674698, 0.96385542168674698, 0.95983935742971882, 0.97590361445783136, 0.97991967871485941, 0.95983935742971882, 0.97991967871485941, 0.90763052208835338, 0.99598393574297184]\n",
      "[[ 0  0  3 ...,  0  0 21]\n",
      " [ 0  0  3 ...,  0  0 19]\n",
      " [ 0  0  2 ...,  0  0 19]\n",
      " ..., \n",
      " [ 0  0  1 ...,  0  0  3]\n",
      " [ 0  0  0 ...,  0  0  0]\n",
      " [ 0  0  0 ...,  0  0  0]]\n",
      "[[False False  True ..., False False  True]\n",
      " [False False  True ..., False False  True]\n",
      " [False False  True ..., False False  True]\n",
      " ..., \n",
      " [False False  True ..., False False  True]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.99130434782608701, 0.99130434782608701, 0.9956521739130435, 0.9956521739130435, 0.9956521739130435, 0.99130434782608701, 0.9826086956521739, 0.9826086956521739, 0.9652173913043478, 0.9869565217391304, 0.9652173913043478, 0.9695652173913043, 0.9652173913043478, 0.87391304347826082, 0.9956521739130435]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.98882681564245811, 0.99022346368715086, 0.994413407821229, 0.9972067039106145, 0.99022346368715086, 0.99022346368715086, 0.98184357541899436, 0.98184357541899436, 0.98463687150837986, 0.99022346368715086, 0.98324022346368711, 0.98743016759776536, 0.99162011173184361, 0.96927374301675973, 0.99860335195530725]\n",
      "[[0 1 0 ..., 0 1 0]\n",
      " [0 0 0 ..., 0 1 0]\n",
      " [0 0 0 ..., 0 1 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 1]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False  True False ..., False  True False]\n",
      " [False False False ..., False  True False]\n",
      " [False False False ..., False  True False]\n",
      " ..., \n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.98958333333333337, 0.98958333333333337, 0.98958333333333337, 0.97916666666666663, 0.98958333333333337, 0.98958333333333337, 0.96875, 0.98958333333333337, 0.94791666666666663, 0.97916666666666663, 0.96875, 0.96875, 0.97916666666666663, 0.8125, 1.0]\n",
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      "   10   0   0   0   0   0   4   0   0   0   0   0   0   0   0   0   0   0\n",
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      "    0   0   0   0   0   0]]\n",
      "[[False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False  True False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False  True False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False  True False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False False False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False False False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False  True False False\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False  True False False\n",
      "  False False  True False False False False False False  True False  True\n",
      "  False False False False  True False False False False False False False\n",
      "   True False False False False False  True False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False False False False\n",
      "  False False False False False False False False False False False False\n",
      "  False False False False False False False False False False False False\n",
      "   True False False False False False False False False False False False\n",
      "  False False False False False False False False False False False False]\n",
      " [False False False False  True False False False False False False False\n",
      "  False False False False False False False False False False False False\n",
      "  False False False False False False False False False False False False\n",
      "   True False False False False False False False False False False False\n",
      "  False False False False False False False False False False False False]]\n",
      "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.98333333333333328, 0.94999999999999996, 0.96666666666666667, 1.0, 0.98333333333333328, 0.96666666666666667, 0.98333333333333328, 0.90000000000000002, 1.0]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.98811881188118811, 0.99603960396039604, 0.9927392739273927, 0.99867986798679864, 0.99339933993399343, 0.99339933993399343, 0.99207920792079207, 0.98811881188118811, 0.9927392739273927, 0.99009900990099009, 0.98877887788778873, 0.96237623762376234, 0.97227722772277225, 0.98283828382838279, 0.95577557755775577, 0.96501650165016506, 0.97953795379537956, 0.94323432343234326]\n",
      "[[7 0 0 ..., 0 0 0]\n",
      " [7 0 0 ..., 0 0 1]\n",
      " [8 0 0 ..., 0 0 1]\n",
      " ..., \n",
      " [3 0 0 ..., 0 0 0]\n",
      " [2 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False  True]\n",
      " [ True False False ..., False False  True]\n",
      " ..., \n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.99196787148594379, 0.99196787148594379, 0.98795180722891562, 0.99598393574297184, 0.99196787148594379, 0.98795180722891562, 0.99196787148594379, 0.98795180722891562, 0.99196787148594379, 0.98393574297188757, 0.99598393574297184, 0.91967871485943775, 0.94779116465863456, 0.98795180722891562, 0.92369477911646591, 0.93975903614457834, 0.95983935742971882, 0.93172690763052213]\n",
      "[[0 0 0 ..., 0 1 3]\n",
      " [0 0 0 ..., 0 1 2]\n",
      " [0 0 0 ..., 0 1 2]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 2]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False  True  True]\n",
      " [False False False ..., False  True  True]\n",
      " [False False False ..., False  True  True]\n",
      " ..., \n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.9826086956521739, 0.9826086956521739, 0.9869565217391304, 0.9956521739130435, 0.9826086956521739, 0.94782608695652171, 0.9826086956521739, 0.97391304347826091, 0.97826086956521741, 0.9695652173913043, 0.95652173913043481, 0.96086956521739131, 0.95217391304347831, 0.97391304347826091, 0.90434782608695652, 0.93478260869565222, 0.95652173913043481, 0.95652173913043481]\n",
      "[[0 0 0 ..., 0 4 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 7 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 1 1 0]]\n",
      "[[False False False ..., False  True False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ...,  True False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ...,  True  True False]]\n",
      "[0.98743016759776536, 0.99860335195530725, 0.99162011173184361, 0.99581005586592175, 0.99581005586592175, 0.99581005586592175, 0.99301675977653636, 0.98882681564245811, 0.994413407821229, 0.994413407821229, 0.99162011173184361, 0.99022346368715086, 0.99022346368715086, 1.0, 0.95949720670391059, 0.95949720670391059, 0.96787709497206709, 0.91620111731843579]\n",
      "[[0 0 0 ..., 0 0 2]\n",
      " [0 0 0 ..., 0 0 2]\n",
      " [0 0 0 ..., 0 0 2]\n",
      " ..., \n",
      " [0 8 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 8 0 ..., 1 0 0]]\n",
      "[[False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " ..., \n",
      " [False  True False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False  True False ...,  True False False]]\n",
      "[0.97916666666666663, 1.0, 0.97916666666666663, 0.98958333333333337, 1.0, 0.97916666666666663, 0.97916666666666663, 0.98958333333333337, 1.0, 0.97916666666666663, 0.97916666666666663, 0.94791666666666663, 0.94791666666666663, 0.97916666666666663, 0.875, 0.90625, 0.91666666666666663, 0.82291666666666663]\n",
      "[[0 0 0 ..., 0 0 1]\n",
      " [0 0 0 ..., 0 0 1]\n",
      " [0 0 0 ..., 0 0 1]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " [False False False ..., False False  True]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[1.0, 1.0, 0.98333333333333328, 1.0, 0.94999999999999996, 1.0, 0.98333333333333328, 1.0, 0.98333333333333328, 0.94999999999999996, 0.93333333333333335, 0.94999999999999996, 0.98333333333333328, 0.94999999999999996, 0.91666666666666663, 0.93333333333333335, 0.96666666666666667, 0.93333333333333335]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.99339933993399343, 0.98679867986798675, 0.96171617161716172, 0.9782178217821782, 0.99207920792079207, 0.98019801980198018, 0.99141914191419145, 0.98481848184818477, 0.97755775577557757, 0.98283828382838279, 0.97623762376237622, 0.96435643564356432, 0.93597359735973595, 0.96963696369636965, 0.95841584158415838, 0.96171617161716172, 0.92673267326732678, 0.98283828382838279, 0.99537953795379541]\n",
      "[[5 0 0 ..., 0 0 0]\n",
      " [5 0 0 ..., 0 0 0]\n",
      " [5 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [1 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " [ True False False ..., False False False]\n",
      " ..., \n",
      " [ True False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.9718875502008032, 0.98393574297188757, 0.93172690763052213, 0.99598393574297184, 0.97991967871485941, 0.96385542168674698, 0.9718875502008032, 0.99196787148594379, 0.97991967871485941, 0.96385542168674698, 0.96385542168674698, 0.95180722891566261, 0.86746987951807231, 0.95582329317269077, 0.91566265060240959, 0.96787148594377514, 0.91164658634538154, 0.90763052208835338, 0.95582329317269077]\n",
      "[[0 0 0 ..., 3 1 7]\n",
      " [0 0 0 ..., 3 1 9]\n",
      " [0 0 0 ..., 3 1 7]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ...,  True  True  True]\n",
      " [False False False ...,  True  True  True]\n",
      " [False False False ...,  True  True  True]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.9956521739130435, 0.9826086956521739, 0.97826086956521741, 0.97391304347826091, 0.9826086956521739, 0.97826086956521741, 0.99130434782608701, 0.99130434782608701, 0.9826086956521739, 0.9956521739130435, 0.9826086956521739, 0.96086956521739131, 0.90434782608695652, 0.9695652173913043, 0.94782608695652171, 0.9695652173913043, 0.93478260869565222, 0.93478260869565222, 0.99130434782608701]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.99301675977653636, 0.99022346368715086, 0.97765363128491622, 0.98324022346368711, 0.99301675977653636, 0.97486033519553073, 0.98184357541899436, 0.98463687150837986, 0.98603351955307261, 0.98603351955307261, 0.98882681564245811, 0.97206703910614523, 0.95251396648044695, 0.96508379888268159, 0.96368715083798884, 0.95810055865921784, 0.93156424581005581, 0.99301675977653636, 0.99860335195530725]\n",
      "[[0 0 0 ..., 0 1 0]\n",
      " [0 0 0 ..., 0 1 0]\n",
      " [0 0 0 ..., 0 1 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False  True False]\n",
      " [False False False ..., False  True False]\n",
      " [False False False ..., False  True False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[1.0, 0.97916666666666663, 0.9375, 0.98958333333333337, 1.0, 0.97916666666666663, 0.96875, 1.0, 0.96875, 0.98958333333333337, 0.97916666666666663, 0.96875, 0.88541666666666663, 0.92708333333333337, 0.85416666666666663, 0.97916666666666663, 0.86458333333333337, 0.89583333333333337, 1.0]\n",
      "[[0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " ..., \n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]\n",
      " [0 0 0 ..., 0 0 0]]\n",
      "[[False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " ..., \n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]\n",
      " [False False False ..., False False False]]\n",
      "[0.96666666666666667, 0.96666666666666667, 0.94999999999999996, 0.98333333333333328, 1.0, 0.94999999999999996, 1.0, 1.0, 0.96666666666666667, 1.0, 1.0, 0.98333333333333328, 0.90000000000000002, 0.96666666666666667, 0.93333333333333335, 0.94999999999999996, 0.93333333333333335, 0.94999999999999996, 0.98333333333333328]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb28831f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get dates for the universe\n",
    "file_dates = {\n",
    "    ticker: [ten_k['file_date'] for ten_k in ten_ks]\n",
    "    for ticker, ten_ks in ten_ks_by_ticker.items()}  \n",
    "\n",
    "jaccard_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_jaccard_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_bow_ten_ks.items()}\n",
    "\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [jaccard_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Jaccard Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF\n",
    "using the sentiment word lists, let's generate sentiment TFIDF from the 10-k documents. Implement `get_tfidf` to generate TFIDF from each document, using sentiment words as the terms. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "\n",
    "def get_tfidf(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate TFIDF values from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "#     print(sentiment_words)\n",
    "#     print(docs)\n",
    "    v = TfidfVectorizer(vocabulary=sentiment_words)\n",
    "#     tfidf = v.fit_transform(docs)\n",
    "#     print(tfidf)\n",
    "#     print(tfidf.toarray())\n",
    "    tfidf = v.fit_transform(docs).toarray()\n",
    "    return tfidf\n",
    "    \n",
    "    \n",
    "    return None\n",
    "\n",
    "\n",
    "project_tests.test_get_tfidf(get_tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_tfidf` function, let's generate the TFIDF values for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    positive: '[[ 0.22288432  0.          0.         ...,  0.   ...\n",
      "    uncertainty: '[[ 0.          0.          0.         ...,  0.005...\n",
      "    litigious: '[[ 0.  0.  0. ...,  0.  0.  0.]\\n [ 0.  0.  0. .....\n",
      "    constraining: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    interesting: '[[ 0.01673784  0.          0.         ...,  0.   ...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_tfidf_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['file_lemma']) for ten_k in ten_ks]\n",
    "    \n",
    "    sentiment_tfidf_ten_ks[ticker] = {\n",
    "        sentiment: get_tfidf(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}\n",
    "\n",
    "    \n",
    "project_helper.print_ten_k_data([sentiment_tfidf_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cosine Similarity\n",
    "Using the TFIDF values, we'll calculate the cosine similarity and plot it over time. Implement `get_cosine_similarity` to return the cosine similarities between each tick in time. Since the input, `tfidf_matrix`, is a TFIDF vector for each time period in order, you just need to computer the cosine similarities for each neighboring vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.75093767  0.        ]\n",
      " [ 1.          0.        ]\n",
      " [ 0.          1.        ]]\n",
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "\n",
    "def get_cosine_similarity(tfidf_matrix):\n",
    "    \"\"\"\n",
    "    Get cosine similarities for each neighboring TFIDF vector/document\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    cosine_similarities : list of float\n",
    "        Cosine similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "#     print(tfidf_matrix)\n",
    "    m1 = tfidf_matrix[0:]\n",
    "    m2 = tfidf_matrix[1:]\n",
    "    cos_sim = cosine_similarity(m1,m2)\n",
    "    print(cos_sim)\n",
    "    cos_sim_list = list(cos_sim[0])\n",
    "    return cos_sim_list\n",
    "\n",
    "\n",
    "project_tests.test_get_cosine_similarity(get_cosine_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the cosine similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.98065125  0.95001565  0.93176312  0.89701433  0.85838968  0.89466195\n",
      "   0.91155016  0.90950097  0.90340218  0.89177823  0.86877236  0.85226339\n",
      "   0.74405432  0.77896335  0.80089009  0.77031453]\n",
      " [ 1.          0.95951741  0.94218859  0.90648091  0.86237166  0.89179621\n",
      "   0.90574701  0.89369966  0.87521553  0.86798128  0.85044926  0.84137208\n",
      "   0.7562598   0.78458687  0.78942326  0.76782938]\n",
      " [ 0.95951741  1.          0.98838551  0.96915463  0.92589154  0.93171412\n",
      "   0.91086194  0.83610834  0.82372932  0.80654782  0.85468422  0.84827138\n",
      "   0.66108672  0.68717047  0.68075894  0.66729149]\n",
      " [ 0.94218859  0.98838551  1.          0.97472377  0.92743627  0.93181201\n",
      "   0.90923588  0.82920628  0.81858051  0.79740427  0.85436735  0.84965482\n",
      "   0.65457522  0.68740079  0.66913605  0.6499519 ]\n",
      " [ 0.90648091  0.96915463  0.97472377  1.          0.975851    0.96393239\n",
      "   0.92290942  0.80916289  0.79704647  0.77601369  0.85747506  0.84327441\n",
      "   0.57688773  0.61156033  0.61316394  0.60647596]\n",
      " [ 0.86237166  0.92589154  0.92743627  0.975851    1.          0.97658031\n",
      "   0.94001975  0.81968983  0.80883821  0.78170318  0.85588107  0.82141533\n",
      "   0.51738419  0.5764593   0.57267159  0.55480113]\n",
      " [ 0.89179621  0.93171412  0.93181201  0.96393239  0.97658031  1.\n",
      "   0.98103103  0.87626066  0.86249053  0.83672515  0.88548687  0.84356535\n",
      "   0.54362477  0.60129493  0.61546778  0.59711039]\n",
      " [ 0.90574701  0.91086194  0.90923588  0.92290942  0.94001975  0.98103103\n",
      "   1.          0.93653965  0.92285504  0.90065669  0.90844869  0.85639581\n",
      "   0.59205581  0.66211155  0.67889057  0.64628759]\n",
      " [ 0.89369966  0.83610834  0.82920628  0.80916289  0.81968983  0.87626066\n",
      "   0.93653965  1.          0.98808037  0.98012057  0.91176078  0.84960571\n",
      "   0.70074884  0.77599173  0.79386902  0.7405412 ]\n",
      " [ 0.87521553  0.82372932  0.81858051  0.79704647  0.80883821  0.86249053\n",
      "   0.92285504  0.98808037  1.          0.98288202  0.91135562  0.84480416\n",
      "   0.68841677  0.772476    0.78402002  0.72631644]\n",
      " [ 0.86798128  0.80654782  0.79740427  0.77601369  0.78170318  0.83672515\n",
      "   0.90065669  0.98012057  0.98288202  1.          0.92710892  0.8729569\n",
      "   0.75197249  0.81338256  0.82647655  0.77524575]\n",
      " [ 0.85044926  0.85468422  0.85436735  0.85747506  0.85588107  0.88548687\n",
      "   0.90844869  0.91176078  0.91135562  0.92710892  1.          0.95953508\n",
      "   0.71419203  0.77031554  0.74617329  0.7120773 ]\n",
      " [ 0.84137208  0.84827138  0.84965482  0.84327441  0.82141533  0.84356535\n",
      "   0.85639581  0.84960571  0.84480416  0.8729569   0.95953508  1.\n",
      "   0.7980424   0.82330011  0.77066553  0.74418543]\n",
      " [ 0.7562598   0.66108672  0.65457522  0.57688773  0.51738419  0.54362477\n",
      "   0.59205581  0.70074884  0.68841677  0.75197249  0.71419203  0.7980424\n",
      "   1.          0.95749124  0.88405076  0.84247836]\n",
      " [ 0.78458687  0.68717047  0.68740079  0.61156033  0.5764593   0.60129493\n",
      "   0.66211155  0.77599173  0.772476    0.81338256  0.77031554  0.82330011\n",
      "   0.95749124  1.          0.87599369  0.81492881]\n",
      " [ 0.78942326  0.68075894  0.66913605  0.61316394  0.57267159  0.61546778\n",
      "   0.67889057  0.79386902  0.78402002  0.82647655  0.74617329  0.77066553\n",
      "   0.88405076  0.87599369  1.          0.92859157]\n",
      " [ 0.76782938  0.66729149  0.6499519   0.60647596  0.55480113  0.59711039\n",
      "   0.64628759  0.7405412   0.72631644  0.77524575  0.7120773   0.74418543\n",
      "   0.84247836  0.81492881  0.92859157  1.        ]]\n",
      "[[ 0.99505527  0.99007419  0.9869179   0.96695821  0.94973034  0.92479147\n",
      "   0.94341074  0.94191649  0.94883011  0.93446624  0.88386964  0.83711934\n",
      "   0.6752215   0.63063168  0.43854812  0.79269584]\n",
      " [ 1.          0.99446855  0.99151731  0.96746351  0.9468497   0.92194854\n",
      "   0.93969145  0.93513451  0.93777666  0.9241252   0.87310571  0.82775423\n",
      "   0.67008249  0.61978783  0.43919544  0.79272174]\n",
      " [ 0.99446855  1.          0.99288655  0.97115945  0.95455021  0.93271623\n",
      "   0.94758195  0.92918955  0.94184001  0.9352179   0.88783783  0.84621991\n",
      "   0.69541453  0.64319928  0.44356447  0.78906734]\n",
      " [ 0.99151731  0.99288655  1.          0.98234804  0.96652269  0.94717023\n",
      "   0.95948491  0.95380861  0.95635909  0.94317058  0.90093897  0.86619614\n",
      "   0.72687256  0.68056563  0.44898685  0.80048891]\n",
      " [ 0.96746351  0.97115945  0.98234804  1.          0.97346488  0.95776435\n",
      "   0.96652187  0.96689552  0.96262634  0.94559301  0.9032502   0.87659138\n",
      "   0.75083454  0.70155187  0.45940618  0.8199606 ]\n",
      " [ 0.9468497   0.95455021  0.96652269  0.97346488  1.          0.99093251\n",
      "   0.98729956  0.96522028  0.98057246  0.97841406  0.96216636  0.94382775\n",
      "   0.83721732  0.78953913  0.48225262  0.80253046]\n",
      " [ 0.92194854  0.93271623  0.94717023  0.95776435  0.99093251  1.\n",
      "   0.99092292  0.9597879   0.96864031  0.96853745  0.95606226  0.949498\n",
      "   0.86744026  0.82140369  0.48869796  0.80433131]\n",
      " [ 0.93969145  0.94758195  0.95948491  0.96652187  0.98729956  0.99092292\n",
      "   1.          0.97388222  0.97840667  0.97117846  0.94274891  0.92444892\n",
      "   0.82687357  0.78168889  0.47293919  0.80815392]\n",
      " [ 0.93513451  0.92918955  0.95380861  0.96689552  0.96522028  0.9597879\n",
      "   0.97388222  1.          0.97711539  0.94475774  0.90299443  0.88286389\n",
      "   0.77520692  0.73706394  0.4585311   0.83620102]\n",
      " [ 0.93777666  0.94184001  0.95635909  0.96262634  0.98057246  0.96864031\n",
      "   0.97840667  0.97711539  1.          0.98834936  0.95093671  0.92194456\n",
      "   0.80388896  0.75981796  0.44808296  0.78125437]\n",
      " [ 0.9241252   0.9352179   0.94317058  0.94559301  0.97841406  0.96853745\n",
      "   0.97117846  0.94475774  0.98834936  1.          0.97359502  0.94438844\n",
      "   0.8275206   0.77369307  0.45345805  0.74551643]\n",
      " [ 0.87310571  0.88783783  0.90093897  0.9032502   0.96216636  0.95606226\n",
      "   0.94274891  0.90299443  0.95093671  0.97359502  1.          0.98483282\n",
      "   0.89432385  0.85151976  0.49338718  0.72408997]\n",
      " [ 0.82775423  0.84621991  0.86619614  0.87659138  0.94382775  0.949498\n",
      "   0.92444892  0.88286389  0.92194456  0.94438844  0.98483282  1.\n",
      "   0.94692438  0.91108164  0.52957896  0.73094088]\n",
      " [ 0.67008249  0.69541453  0.72687256  0.75083454  0.83721732  0.86744026\n",
      "   0.82687357  0.77520692  0.80388896  0.8275206   0.89432385  0.94692438\n",
      "   1.          0.98113687  0.56300311  0.66100449]\n",
      " [ 0.61978783  0.64319928  0.68056563  0.70155187  0.78953913  0.82140369\n",
      "   0.78168889  0.73706394  0.75981796  0.77369307  0.85151976  0.91108164\n",
      "   0.98113687  1.          0.5773853   0.6196866 ]\n",
      " [ 0.43919544  0.44356447  0.44898685  0.45940618  0.48225262  0.48869796\n",
      "   0.47293919  0.4585311   0.44808296  0.45345805  0.49338718  0.52957896\n",
      "   0.56300311  0.5773853   1.          0.56878244]\n",
      " [ 0.79272174  0.78906734  0.80048891  0.8199606   0.80253046  0.80433131\n",
      "   0.80815392  0.83620102  0.78125437  0.74551643  0.72408997  0.73094088\n",
      "   0.66100449  0.6196866   0.56878244  1.        ]]\n",
      "[[ 0.99893168  0.9965654   0.99384119  0.98976849  0.98470062  0.98178293\n",
      "   0.98587908  0.98248134  0.98126443  0.97697736  0.97700112  0.9742183\n",
      "   0.95546071  0.95574791  0.95604954  0.88164745]\n",
      " [ 1.          0.99713402  0.99462077  0.99174886  0.98721896  0.9849763\n",
      "   0.98781192  0.98434837  0.98012703  0.9774111   0.97595028  0.97110156\n",
      "   0.95134657  0.95065532  0.95049751  0.869238  ]\n",
      " [ 0.99713402  1.          0.99867142  0.99194927  0.98231117  0.97943444\n",
      "   0.98214597  0.97995308  0.97396585  0.96931624  0.96901484  0.9656638\n",
      "   0.94691817  0.94745286  0.9475587   0.87379254]\n",
      " [ 0.99462077  0.99867142  1.          0.99346562  0.98262917  0.97985678\n",
      "   0.98223193  0.98106617  0.97463543  0.96962712  0.96876805  0.96538879\n",
      "   0.94765098  0.94757678  0.94617298  0.87430733]\n",
      " [ 0.99174886  0.99194927  0.99346562  1.          0.9958991   0.9932577\n",
      "   0.99470096  0.9942218   0.98618714  0.97893795  0.97253689  0.96406225\n",
      "   0.94540997  0.94730318  0.94606255  0.87523434]\n",
      " [ 0.98721896  0.98231117  0.98262917  0.9958991   1.          0.99830264\n",
      "   0.9982832   0.99703952  0.98765283  0.97892813  0.97020253  0.95896301\n",
      "   0.93874649  0.94188169  0.94005101  0.86585801]\n",
      " [ 0.9849763   0.97943444  0.97985678  0.9932577   0.99830264  1.\n",
      "   0.9985051   0.99599265  0.98425705  0.97833132  0.96750343  0.95512617\n",
      "   0.93225607  0.93683075  0.92989457  0.84981096]\n",
      " [ 0.98781192  0.98214597  0.98223193  0.99470096  0.9982832   0.9985051\n",
      "   1.          0.99729679  0.99104646  0.98478381  0.97440973  0.96322516\n",
      "   0.94138755  0.94566033  0.93913904  0.86328293]\n",
      " [ 0.98434837  0.97995308  0.98106617  0.9942218   0.99703952  0.99599265\n",
      "   0.99729679  1.          0.99226014  0.98368925  0.97465961  0.9651177\n",
      "   0.945208    0.94942154  0.944399    0.87468911]\n",
      " [ 0.98012703  0.97396585  0.97463543  0.98618714  0.98765283  0.98425705\n",
      "   0.99104646  0.99226014  1.          0.99121486  0.9840226   0.97786792\n",
      "   0.96302904  0.96481805  0.95876445  0.89828067]\n",
      " [ 0.9774111   0.96931624  0.96962712  0.97893795  0.97892813  0.97833132\n",
      "   0.98478381  0.98368925  0.99121486  1.          0.99245948  0.98423053\n",
      "   0.96615686  0.96684973  0.9481955   0.87066691]\n",
      " [ 0.97595028  0.96901484  0.96876805  0.97253689  0.97020253  0.96750343\n",
      "   0.97440973  0.97465961  0.9840226   0.99245948  1.          0.99157853\n",
      "   0.97807123  0.97444815  0.95927927  0.88991277]\n",
      " [ 0.97110156  0.9656638   0.96538879  0.96406225  0.95896301  0.95512617\n",
      "   0.96322516  0.9651177   0.97786792  0.98423053  0.99157853  1.\n",
      "   0.9885654   0.98263008  0.96722921  0.90681139]\n",
      " [ 0.95134657  0.94691817  0.94765098  0.94540997  0.93874649  0.93225607\n",
      "   0.94138755  0.945208    0.96302904  0.96615686  0.97807123  0.9885654\n",
      "   1.          0.99325338  0.97967622  0.93578173]\n",
      " [ 0.95065532  0.94745286  0.94757678  0.94730318  0.94188169  0.93683075\n",
      "   0.94566033  0.94942154  0.96481805  0.96684973  0.97444815  0.98263008\n",
      "   0.99325338  1.          0.97918828  0.93985017]\n",
      " [ 0.95049751  0.9475587   0.94617298  0.94606255  0.94005101  0.92989457\n",
      "   0.93913904  0.944399    0.95876445  0.9481955   0.95927927  0.96722921\n",
      "   0.97967622  0.97918828  1.          0.96767307]\n",
      " [ 0.869238    0.87379254  0.87430733  0.87523434  0.86585801  0.84981096\n",
      "   0.86328293  0.87468911  0.89828067  0.87066691  0.88991277  0.90681139\n",
      "   0.93578173  0.93985017  0.96767307  1.        ]]\n",
      "[[ 0.98736177  0.97771263  0.95309728  0.90900914  0.8747436   0.90145331\n",
      "   0.86886536  0.86085959  0.8567665   0.85375098  0.82736187  0.78506065\n",
      "   0.71841457  0.68812197  0.6089252   0.62428457]\n",
      " [ 1.          0.98163767  0.96033459  0.91800614  0.89554975  0.92426237\n",
      "   0.90404404  0.90381699  0.89732097  0.88173168  0.82638474  0.78178742\n",
      "   0.72011776  0.70077968  0.66101073  0.65020854]\n",
      " [ 0.98163767  1.          0.98511457  0.96218201  0.93019903  0.9341845\n",
      "   0.8855727   0.84935898  0.84654898  0.83879981  0.85063311  0.82565388\n",
      "   0.70037015  0.68146204  0.59728364  0.61664003]\n",
      " [ 0.96033459  0.98511457  1.          0.97560401  0.94549362  0.94753718\n",
      "   0.89875755  0.8535084   0.85575571  0.85631484  0.8847292   0.86393551\n",
      "   0.72404438  0.70475953  0.60687023  0.63840823]\n",
      " [ 0.91800614  0.96218201  0.97560401  1.          0.97771547  0.94818784\n",
      "   0.8772787   0.80747249  0.82215796  0.81609268  0.88424508  0.87056868\n",
      "   0.69113865  0.67631342  0.55958653  0.61298268]\n",
      " [ 0.89554975  0.93019903  0.94549362  0.97771547  1.          0.96252822\n",
      "   0.90683774  0.84709513  0.86485965  0.85552327  0.90410859  0.88735922\n",
      "   0.71295282  0.69896904  0.61474735  0.65567507]\n",
      " [ 0.92426237  0.9341845   0.94753718  0.94818784  0.96252822  1.\n",
      "   0.96403753  0.9123351   0.91989066  0.90472482  0.90396377  0.88051547\n",
      "   0.73089762  0.7093303   0.6944909   0.71299524]\n",
      " [ 0.90404404  0.8855727   0.89875755  0.8772787   0.90683774  0.96403753\n",
      "   1.          0.95051644  0.95170042  0.93079888  0.86721878  0.83713676\n",
      "   0.74032723  0.70949415  0.7445054   0.74338571]\n",
      " [ 0.90381699  0.84935898  0.8535084   0.80747249  0.84709513  0.9123351\n",
      "   0.95051644  1.          0.9875002   0.96984576  0.84049417  0.78969074\n",
      "   0.77294697  0.76182047  0.81632673  0.7877116 ]\n",
      " [ 0.89732097  0.84654898  0.85575571  0.82215796  0.86485965  0.91989066\n",
      "   0.95170042  0.9875002   1.          0.97470655  0.86286346  0.8052983\n",
      "   0.77434182  0.74207347  0.79527647  0.77493341]\n",
      " [ 0.88173168  0.83879981  0.85631484  0.81609268  0.85552327  0.90472482\n",
      "   0.93079888  0.96984576  0.97470655  1.          0.90437859  0.85364351\n",
      "   0.83598731  0.81317528  0.79299383  0.78488799]\n",
      " [ 0.82638474  0.85063311  0.8847292   0.88424508  0.90410859  0.90396377\n",
      "   0.86721878  0.84049417  0.86286346  0.90437859  1.          0.96145964\n",
      "   0.82446466  0.78938998  0.64980098  0.72546496]\n",
      " [ 0.78178742  0.82565388  0.86393551  0.87056868  0.88735922  0.88051547\n",
      "   0.83713676  0.78969074  0.8052983   0.85364351  0.96145964  1.\n",
      "   0.82738817  0.79900739  0.60905136  0.69614354]\n",
      " [ 0.72011776  0.70037015  0.72404438  0.69113865  0.71295282  0.73089762\n",
      "   0.74032723  0.77294697  0.77434182  0.83598731  0.82446466  0.82738817\n",
      "   1.          0.92993737  0.65922655  0.70501113]\n",
      " [ 0.70077968  0.68146204  0.70475953  0.67631342  0.69896904  0.7093303\n",
      "   0.70949415  0.76182047  0.74207347  0.81317528  0.78938998  0.79900739\n",
      "   0.92993737  1.          0.68825749  0.72072216]\n",
      " [ 0.66101073  0.59728364  0.60687023  0.55958653  0.61474735  0.6944909\n",
      "   0.7445054   0.81632673  0.79527647  0.79299383  0.64980098  0.60905136\n",
      "   0.65922655  0.68825749  1.          0.83411631]\n",
      " [ 0.65020854  0.61664003  0.63840823  0.61298268  0.65567507  0.71299524\n",
      "   0.74338571  0.7877116   0.77493341  0.78488799  0.72546496  0.69614354\n",
      "   0.70501113  0.72072216  0.83411631  1.        ]]\n",
      "[[ 0.97871151  0.98905774  0.97160745  0.96249431  0.97659817  0.97381896\n",
      "   0.97126095  0.96448597  0.96208627  0.94345529  0.95288248  0.93059618\n",
      "   0.91449081  0.91387298  0.90336403  0.88030078]\n",
      " [ 1.          0.99340265  0.99120546  0.98903225  0.98773441  0.98096064\n",
      "   0.97425567  0.9524372   0.93719883  0.92143335  0.9254153   0.90708213\n",
      "   0.90198436  0.93253832  0.86460788  0.86670162]\n",
      " [ 0.99340265  1.          0.98863507  0.98299489  0.98890178  0.98585021\n",
      "   0.97823077  0.96054038  0.95019966  0.92947934  0.93637302  0.91692781\n",
      "   0.91165653  0.93189455  0.89375036  0.88211862]\n",
      " [ 0.99120546  0.98863507  1.          0.99426665  0.9954208   0.99061016\n",
      "   0.98269158  0.95903841  0.94501059  0.93323735  0.93799376  0.91833017\n",
      "   0.91180453  0.93328368  0.87967134  0.88001725]\n",
      " [ 0.98903225  0.98299489  0.99426665  1.          0.99179779  0.99081783\n",
      "   0.98589264  0.955171    0.93802551  0.92070622  0.92771929  0.89596224\n",
      "   0.88084412  0.90838716  0.88781284  0.88666747]\n",
      " [ 0.98773441  0.98890178  0.9954208   0.99179779  1.          0.99491778\n",
      "   0.9840102   0.96077854  0.94859852  0.92895529  0.94035604  0.91568467\n",
      "   0.9081916   0.92694598  0.89276883  0.88638722]\n",
      " [ 0.98096064  0.98585021  0.99061016  0.99081783  0.99491778  1.\n",
      "   0.99274524  0.9668865   0.95607891  0.93498723  0.94688621  0.91437019\n",
      "   0.89799506  0.9146621   0.9143866   0.89974708]\n",
      " [ 0.97425567  0.97823077  0.98269158  0.98589264  0.9840102   0.99274524\n",
      "   1.          0.98172983  0.97153431  0.95400105  0.96189964  0.92250375\n",
      "   0.88590484  0.89199135  0.91576099  0.895453  ]\n",
      " [ 0.9524372   0.96054038  0.95903841  0.955171    0.96077854  0.9668865\n",
      "   0.98172983  1.          0.99453787  0.98003257  0.98034653  0.95458734\n",
      "   0.90904956  0.89303343  0.90364618  0.87332254]\n",
      " [ 0.93719883  0.95019966  0.94501059  0.93802551  0.94859852  0.95607891\n",
      "   0.97153431  0.99453787  1.          0.98880301  0.98570323  0.96814165\n",
      "   0.92438773  0.89497965  0.90035085  0.87190682]\n",
      " [ 0.92143335  0.92947934  0.93323735  0.92070622  0.92895529  0.93498723\n",
      "   0.95400105  0.98003257  0.98880301  1.          0.98296177  0.98249468\n",
      "   0.9411545   0.89839083  0.86645081  0.84527541]\n",
      " [ 0.9254153   0.93637302  0.93799376  0.92771929  0.94035604  0.94688621\n",
      "   0.96189964  0.98034653  0.98570323  0.98296177  1.          0.97588647\n",
      "   0.92235223  0.89083945  0.8933837   0.86714743]\n",
      " [ 0.90708213  0.91692781  0.91833017  0.89596224  0.91568467  0.91437019\n",
      "   0.92250375  0.95458734  0.96814165  0.98249468  0.97588647  1.\n",
      "   0.97563016  0.93333388  0.82756441  0.8126883 ]\n",
      " [ 0.90198436  0.91165653  0.91180453  0.88084412  0.9081916   0.89799506\n",
      "   0.88590484  0.90904956  0.92438773  0.9411545   0.92235223  0.97563016\n",
      "   1.          0.96723531  0.78327015  0.78180838]\n",
      " [ 0.93253832  0.93189455  0.93328368  0.90838716  0.92694598  0.9146621\n",
      "   0.89199135  0.89303343  0.89497965  0.89839083  0.89083945  0.93333388\n",
      "   0.96723531  1.          0.76138865  0.78372913]\n",
      " [ 0.86460788  0.89375036  0.87967134  0.88781284  0.89276883  0.9143866\n",
      "   0.91576099  0.90364618  0.90035085  0.86645081  0.8933837   0.82756441\n",
      "   0.78327015  0.76138865  1.          0.91050744]\n",
      " [ 0.86670162  0.88211862  0.88001725  0.88666747  0.88638722  0.89974708\n",
      "   0.895453    0.87332254  0.87190682  0.84527541  0.86714743  0.8126883\n",
      "   0.78180838  0.78372913  0.91050744  1.        ]]\n",
      "[[ 0.99896034  0.99712377  0.99497822  0.99259925  0.99468994  0.9906054\n",
      "   0.99091894  0.99650619  0.98080622  0.97961441  0.97856615  0.94421107\n",
      "   0.95279092  0.96925843  0.97948274  0.85428127]\n",
      " [ 1.          0.99800307  0.99789081  0.99587997  0.99536567  0.9941952\n",
      "   0.99373725  0.99687128  0.98290542  0.9814295   0.97629793  0.93701598\n",
      "   0.94816182  0.96397928  0.97511087  0.84364301]\n",
      " [ 0.99800307  1.          0.99841373  0.99671036  0.99861827  0.99549775\n",
      "   0.99629306  0.99809837  0.9887546   0.98822332  0.98539275  0.95199709\n",
      "   0.96137339  0.97261055  0.98068575  0.85251709]\n",
      " [ 0.99789081  0.99841373  1.          0.99908309  0.99698301  0.99810803\n",
      "   0.99767031  0.99695199  0.98903451  0.98775565  0.97941563  0.93920628\n",
      "   0.95183203  0.96474062  0.97473972  0.84289515]\n",
      " [ 0.99587997  0.99671036  0.99908309  1.          0.99698617  0.99935356\n",
      "   0.99912251  0.9970826   0.99215901  0.99080964  0.98132851  0.94120892\n",
      "   0.95360185  0.96607711  0.97663891  0.84987036]\n",
      " [ 0.99536567  0.99861827  0.99698301  0.99698617  1.          0.99673842\n",
      "   0.99713659  0.99787267  0.99090724  0.99069351  0.98912328  0.95818256\n",
      "   0.96648552  0.97726954  0.98333937  0.85800374]\n",
      " [ 0.9941952   0.99549775  0.99810803  0.99935356  0.99673842  1.\n",
      "   0.99882652  0.99562591  0.9912236   0.98971912  0.98042627  0.93953843\n",
      "   0.95193734  0.96414988  0.97477677  0.84130729]\n",
      " [ 0.99373725  0.99629306  0.99767031  0.99912251  0.99713659  0.99882652\n",
      "   1.          0.9976321   0.99600135  0.99482523  0.98679473  0.95049489\n",
      "   0.96104748  0.97061056  0.98198667  0.86124571]\n",
      " [ 0.99687128  0.99809837  0.99695199  0.9970826   0.99787267  0.99562591\n",
      "   0.9976321   1.          0.99307177  0.9918549   0.98902059  0.95754893\n",
      "   0.96606653  0.97751497  0.98792213  0.87540522]\n",
      " [ 0.98290542  0.9887546   0.98903451  0.99215901  0.99090724  0.9912236\n",
      "   0.99600135  0.99307177  1.          0.99957293  0.99277167  0.96526042\n",
      "   0.97292397  0.97702567  0.9885328   0.89319409]\n",
      " [ 0.9814295   0.98822332  0.98775565  0.99080964  0.99069351  0.98971912\n",
      "   0.99482523  0.9918549   0.99957293  1.          0.99389513  0.96829128\n",
      "   0.97506811  0.9780661   0.98833076  0.89327128]\n",
      " [ 0.97629793  0.98539275  0.97941563  0.98132851  0.98912328  0.98042627\n",
      "   0.98679473  0.98902059  0.99277167  0.99389513  1.          0.98794179\n",
      "   0.98889006  0.99067416  0.9951366   0.90978775]\n",
      " [ 0.93701598  0.95199709  0.93920628  0.94120892  0.95818256  0.93953843\n",
      "   0.95049489  0.95754893  0.96526042  0.96829128  0.98794179  1.\n",
      "   0.99428496  0.98908978  0.98377537  0.93498614]\n",
      " [ 0.94816182  0.96137339  0.95183203  0.95360185  0.96648552  0.95193734\n",
      "   0.96104748  0.96606653  0.97292397  0.97506811  0.98889006  0.99428496\n",
      "   1.          0.99544515  0.98423083  0.92456448]\n",
      " [ 0.96397928  0.97261055  0.96474062  0.96607711  0.97726954  0.96414988\n",
      "   0.97061056  0.97751497  0.97702567  0.9780661   0.99067416  0.98908978\n",
      "   0.99544515  1.          0.99007795  0.9228042 ]\n",
      " [ 0.97511087  0.98068575  0.97473972  0.97663891  0.98333937  0.97477677\n",
      "   0.98198667  0.98792213  0.9885328   0.98833076  0.9951366   0.98377537\n",
      "   0.98423083  0.99007795  1.          0.92969286]\n",
      " [ 0.84364301  0.85251709  0.84289515  0.84987036  0.85800374  0.84130729\n",
      "   0.86124571  0.87540522  0.89319409  0.89327128  0.90978775  0.93498614\n",
      "   0.92456448  0.9228042   0.92969286  1.        ]]\n",
      "[[ 0.96954532  0.95728473  0.91682002  0.90296365  0.87806399  0.87681412\n",
      "   0.85592881  0.86605043  0.82736876  0.78397369  0.81644795  0.74991198\n",
      "   0.54331176  0.66654155  0.34147117  0.39284661  0.40959182  0.39224816\n",
      "   0.38751875  0.45378646  0.43571094  0.44665176]\n",
      " [ 1.          0.96275073  0.92696109  0.91441215  0.8969008   0.88436078\n",
      "   0.86122709  0.86508902  0.82949259  0.79020749  0.82795865  0.75661011\n",
      "   0.54060738  0.6512026   0.35784992  0.40540918  0.41610045  0.39678101\n",
      "   0.3954183   0.455837    0.44392952  0.45464648]\n",
      " [ 0.96275073  1.          0.97443031  0.96376545  0.92340844  0.91279994\n",
      "   0.88587378  0.89164342  0.84803253  0.80346793  0.83356006  0.7686393\n",
      "   0.54997387  0.67389493  0.3512257   0.38018977  0.39110995  0.37982243\n",
      "   0.38005466  0.43794187  0.42141255  0.45831516]\n",
      " [ 0.92696109  0.97443031  1.          0.98149252  0.93277934  0.91998886\n",
      "   0.88268479  0.88774387  0.84908626  0.79899616  0.82764486  0.77781574\n",
      "   0.56530118  0.68839364  0.36666011  0.40234919  0.41018462  0.4144551\n",
      "   0.4103533   0.45357607  0.4403009   0.46417165]\n",
      " [ 0.91441215  0.96376545  0.98149252  1.          0.95334546  0.9377175\n",
      "   0.90853975  0.9128143   0.86633219  0.83633117  0.86066989  0.80490002\n",
      "   0.57230487  0.70321224  0.38945251  0.42911896  0.42870628  0.43146378\n",
      "   0.42945157  0.4766579   0.45980902  0.49106648]\n",
      " [ 0.8969008   0.92340844  0.93277934  0.95334546  1.          0.97560329\n",
      "   0.94528669  0.93416855  0.89502666  0.86930634  0.88788194  0.82949214\n",
      "   0.57576779  0.71418751  0.46570828  0.4391337   0.43308945  0.40861321\n",
      "   0.41509328  0.4884679   0.48106278  0.46819686]\n",
      " [ 0.88436078  0.91279994  0.91998886  0.9377175   0.97560329  1.\n",
      "   0.96627909  0.94355268  0.89368369  0.85987075  0.88448714  0.83133196\n",
      "   0.58255059  0.73491551  0.50189108  0.44410726  0.45064632  0.42877121\n",
      "   0.43680345  0.51206599  0.50070721  0.47309274]\n",
      " [ 0.86122709  0.88587378  0.88268479  0.90853975  0.94528669  0.96627909\n",
      "   1.          0.96826012  0.91944803  0.87907984  0.90873265  0.86540249\n",
      "   0.59392129  0.76332487  0.56613732  0.46100589  0.46359023  0.44546127\n",
      "   0.44184595  0.5182253   0.5222625   0.46858262]\n",
      " [ 0.86508902  0.89164342  0.88774387  0.9128143   0.93416855  0.94355268\n",
      "   0.96826012  1.          0.96129774  0.91152858  0.9368966   0.87521113\n",
      "   0.60045593  0.77329143  0.5088765   0.45182015  0.44066574  0.42307471\n",
      "   0.41503748  0.49967414  0.49210488  0.49251713]\n",
      " [ 0.82949259  0.84803253  0.84908626  0.86633219  0.89502666  0.89368369\n",
      "   0.91944803  0.96129774  1.          0.92722917  0.94453005  0.87791535\n",
      "   0.61386351  0.78720376  0.48884589  0.43030507  0.41730015  0.39899247\n",
      "   0.39899144  0.47951039  0.4797837   0.46168893]\n",
      " [ 0.79020749  0.80346793  0.79899616  0.83633117  0.86930634  0.85987075\n",
      "   0.87907984  0.91152858  0.92722917  1.          0.9572544   0.89447064\n",
      "   0.59851442  0.77798856  0.47107564  0.49008823  0.47069619  0.44159471\n",
      "   0.4398257   0.52744318  0.5316063   0.49236195]\n",
      " [ 0.82795865  0.83356006  0.82764486  0.86066989  0.88788194  0.88448714\n",
      "   0.90873265  0.9368966   0.94453005  0.9572544   1.          0.92834085\n",
      "   0.62245054  0.78426003  0.50457598  0.49197799  0.49100521  0.47007747\n",
      "   0.45972761  0.54313677  0.54189568  0.5216378 ]\n",
      " [ 0.75661011  0.7686393   0.77781574  0.80490002  0.82949214  0.83133196\n",
      "   0.86540249  0.87521113  0.87791535  0.89447064  0.92834085  1.\n",
      "   0.74585444  0.85058905  0.5662438   0.5781287   0.58261385  0.56445209\n",
      "   0.53506592  0.62202521  0.62846739  0.46862507]\n",
      " [ 0.54060738  0.54997387  0.56530118  0.57230487  0.57576779  0.58255059\n",
      "   0.59392129  0.60045593  0.61386351  0.59851442  0.62245054  0.74585444\n",
      "   1.          0.78930075  0.40864941  0.40748661  0.42169619  0.40915989\n",
      "   0.39671089  0.45887396  0.46289199  0.34481414]\n",
      " [ 0.6512026   0.67389493  0.68839364  0.70321224  0.71418751  0.73491551\n",
      "   0.76332487  0.77329143  0.78720376  0.77798856  0.78426003  0.85058905\n",
      "   0.78930075  1.          0.59599314  0.56926068  0.56466936  0.57376191\n",
      "   0.55739036  0.62695357  0.62128886  0.38867922]\n",
      " [ 0.35784992  0.3512257   0.36666011  0.38945251  0.46570828  0.50189108\n",
      "   0.56613732  0.5088765   0.48884589  0.47107564  0.50457598  0.5662438\n",
      "   0.40864941  0.59599314  1.          0.72434829  0.69560189  0.66561894\n",
      "   0.63300566  0.67126765  0.68509565  0.31429754]\n",
      " [ 0.40540918  0.38018977  0.40234919  0.42911896  0.4391337   0.44410726\n",
      "   0.46100589  0.45182015  0.43030507  0.49008823  0.49197799  0.5781287\n",
      "   0.40748661  0.56926068  0.72434829  1.          0.80943717  0.76393815\n",
      "   0.71780216  0.73484273  0.73694386  0.31037123]\n",
      " [ 0.41610045  0.39110995  0.41018462  0.42870628  0.43308945  0.45064632\n",
      "   0.46359023  0.44066574  0.41730015  0.47069619  0.49100521  0.58261385\n",
      "   0.42169619  0.56466936  0.69560189  0.80943717  1.          0.83795147\n",
      "   0.79196661  0.80654377  0.78008241  0.34417271]\n",
      " [ 0.39678101  0.37982243  0.4144551   0.43146378  0.40861321  0.42877121\n",
      "   0.44546127  0.42307471  0.39899247  0.44159471  0.47007747  0.56445209\n",
      "   0.40915989  0.57376191  0.66561894  0.76393815  0.83795147  1.\n",
      "   0.90629847  0.87733919  0.83389005  0.39310763]\n",
      " [ 0.3954183   0.38005466  0.4103533   0.42945157  0.41509328  0.43680345\n",
      "   0.44184595  0.41503748  0.39899144  0.4398257   0.45972761  0.53506592\n",
      "   0.39671089  0.55739036  0.63300566  0.71780216  0.79196661  0.90629847\n",
      "   1.          0.90908795  0.86120515  0.40987771]\n",
      " [ 0.455837    0.43794187  0.45357607  0.4766579   0.4884679   0.51206599\n",
      "   0.5182253   0.49967414  0.47951039  0.52744318  0.54313677  0.62202521\n",
      "   0.45887396  0.62695357  0.67126765  0.73484273  0.80654377  0.87733919\n",
      "   0.90908795  1.          0.89403523  0.44521777]\n",
      " [ 0.44392952  0.42141255  0.4403009   0.45980902  0.48106278  0.50070721\n",
      "   0.5222625   0.49210488  0.4797837   0.5316063   0.54189568  0.62846739\n",
      "   0.46289199  0.62128886  0.68509565  0.73694386  0.78008241  0.83389005\n",
      "   0.86120515  0.89403523  1.          0.44784189]\n",
      " [ 0.45464648  0.45831516  0.46417165  0.49106648  0.46819686  0.47309274\n",
      "   0.46858262  0.49251713  0.46168893  0.49236195  0.5216378   0.46862507\n",
      "   0.34481414  0.38867922  0.31429754  0.31037123  0.34417271  0.39310763\n",
      "   0.40987771  0.44521777  0.44784189  1.        ]]\n",
      "[[ 0.99191966  0.99209336  0.98782553  0.87248465  0.88290819  0.88062\n",
      "   0.8482148   0.8028621   0.75494487  0.78385585  0.76418779  0.76246603\n",
      "   0.70612535  0.528449    0.49648476  0.51893447  0.48526306  0.47975005\n",
      "   0.47213381  0.45912813  0.43028406  0.42111089]\n",
      " [ 1.          0.99097596  0.98604048  0.87684924  0.87832194  0.87969843\n",
      "   0.83831797  0.78464565  0.73619513  0.7675743   0.74577889  0.74844412\n",
      "   0.6886326   0.4985265   0.46112197  0.4924191   0.45580301  0.45019642\n",
      "   0.44525396  0.43354042  0.40078591  0.38811857]\n",
      " [ 0.99097596  1.          0.99719617  0.86519346  0.87896002  0.86950733\n",
      "   0.83139971  0.77970187  0.738727    0.76822709  0.74634022  0.75011184\n",
      "   0.69119988  0.50406399  0.46019457  0.49359143  0.4620832   0.45185158\n",
      "   0.45555227  0.44434138  0.41321994  0.40187722]\n",
      " [ 0.98604048  0.99719617  1.          0.85333083  0.87269144  0.85899656\n",
      "   0.82090696  0.76736963  0.72716789  0.75924751  0.74007028  0.74546952\n",
      "   0.67813584  0.47932757  0.43327793  0.46626236  0.43483994  0.42469349\n",
      "   0.43395464  0.42284466  0.39009344  0.37844776]\n",
      " [ 0.87684924  0.86519346  0.85333083  1.          0.9650521   0.96063437\n",
      "   0.94652335  0.89425457  0.8590087   0.89666952  0.8828046   0.88662203\n",
      "   0.84546103  0.62109237  0.59302141  0.63325594  0.59323288  0.57717352\n",
      "   0.59683019  0.57151805  0.53523694  0.5235819 ]\n",
      " [ 0.87832194  0.87896002  0.87269144  0.9650521   1.          0.98139837\n",
      "   0.97235251  0.93653797  0.91038287  0.94284628  0.93177746  0.93774297\n",
      "   0.89515572  0.65604369  0.6243662   0.67041402  0.63750636  0.61989301\n",
      "   0.6381957   0.60606285  0.57425263  0.56333804]\n",
      " [ 0.87969843  0.86950733  0.85899656  0.96063437  0.98139837  1.\n",
      "   0.98690813  0.9592262   0.91125874  0.94020393  0.92908629  0.91955388\n",
      "   0.8935123   0.68553018  0.6742139   0.69534411  0.65668009  0.64751456\n",
      "   0.64159946  0.61207149  0.57542714  0.55568258]\n",
      " [ 0.83831797  0.83139971  0.82090696  0.94652335  0.97235251  0.98690813\n",
      "   1.          0.98265875  0.93768583  0.95422832  0.94564253  0.93123243\n",
      "   0.92172309  0.74602022  0.73444282  0.74905598  0.71499529  0.69881913\n",
      "   0.69340475  0.66322314  0.63891663  0.61968253]\n",
      " [ 0.78464565  0.77970187  0.76736963  0.89425457  0.93653797  0.9592262\n",
      "   0.98265875  1.          0.96177549  0.96700423  0.96097899  0.93630904\n",
      "   0.94341444  0.79900462  0.79586109  0.79103291  0.75869115  0.73797654\n",
      "   0.72137806  0.68705033  0.67618245  0.65774621]\n",
      " [ 0.73619513  0.738727    0.72716789  0.8590087   0.91038287  0.91125874\n",
      "   0.93768583  0.96177549  1.          0.97872982  0.97155949  0.95962153\n",
      "   0.96806173  0.83594528  0.80206892  0.82160526  0.80385682  0.77657027\n",
      "   0.79508875  0.75592771  0.75873364  0.74061758]\n",
      " [ 0.7675743   0.76822709  0.75924751  0.89666952  0.94284628  0.94020393\n",
      "   0.95422832  0.96700423  0.97872982  1.          0.99198941  0.98283514\n",
      "   0.96127643  0.76620619  0.73846265  0.7670354   0.73587136  0.71335347\n",
      "   0.73377535  0.68660649  0.67403901  0.66078264]\n",
      " [ 0.74577889  0.74634022  0.74007028  0.8828046   0.93177746  0.92908629\n",
      "   0.94564253  0.96097899  0.97155949  0.99198941  1.          0.98975192\n",
      "   0.96375754  0.75175081  0.72626856  0.74389849  0.71897479  0.69381537\n",
      "   0.71439028  0.67263487  0.65901864  0.64010961]\n",
      " [ 0.74844412  0.75011184  0.74546952  0.88662203  0.93774297  0.91955388\n",
      "   0.93123243  0.93630904  0.95962153  0.98283514  0.98975192  1.\n",
      "   0.96155988  0.72862912  0.69013024  0.72285435  0.70378561  0.67881713\n",
      "   0.71427818  0.67446215  0.65752698  0.6379262 ]\n",
      " [ 0.6886326   0.69119988  0.67813584  0.84546103  0.89515572  0.8935123\n",
      "   0.92172309  0.94341444  0.96806173  0.96127643  0.96375754  0.96155988\n",
      "   1.          0.86358442  0.81423226  0.84120699  0.83733149  0.79930636\n",
      "   0.81940231  0.78908987  0.78988243  0.76515913]\n",
      " [ 0.4985265   0.50406399  0.47932757  0.62109237  0.65604369  0.68553018\n",
      "   0.74602022  0.79900462  0.83594528  0.76620619  0.75175081  0.72862912\n",
      "   0.86358442  1.          0.93170315  0.92826796  0.95042058  0.91476698\n",
      "   0.90604189  0.89315103  0.93743026  0.91276605]\n",
      " [ 0.46112197  0.46019457  0.43327793  0.59302141  0.6243662   0.6742139\n",
      "   0.73444282  0.79586109  0.80206892  0.73846265  0.72626856  0.69013024\n",
      "   0.81423226  0.93170315  1.          0.92601032  0.91968692  0.89015363\n",
      "   0.85113747  0.83666779  0.86549926  0.83550572]\n",
      " [ 0.4924191   0.49359143  0.46626236  0.63325594  0.67041402  0.69534411\n",
      "   0.74905598  0.79103291  0.82160526  0.7670354   0.74389849  0.72285435\n",
      "   0.84120699  0.92826796  0.92601032  1.          0.96821541  0.94713693\n",
      "   0.92232909  0.90586091  0.90819783  0.87419709]\n",
      " [ 0.45580301  0.4620832   0.43483994  0.59323288  0.63750636  0.65668009\n",
      "   0.71499529  0.75869115  0.80385682  0.73587136  0.71897479  0.70378561\n",
      "   0.83733149  0.95042058  0.91968692  0.96821541  1.          0.9691169\n",
      "   0.95446468  0.93973293  0.94496354  0.90436667]\n",
      " [ 0.45019642  0.45185158  0.42469349  0.57717352  0.61989301  0.64751456\n",
      "   0.69881913  0.73797654  0.77657027  0.71335347  0.69381537  0.67881713\n",
      "   0.79930636  0.91476698  0.89015363  0.94713693  0.9691169   1.\n",
      "   0.96807585  0.95342828  0.92545923  0.87015031]\n",
      " [ 0.44525396  0.45555227  0.43395464  0.59683019  0.6381957   0.64159946\n",
      "   0.69340475  0.72137806  0.79508875  0.73377535  0.71439028  0.71427818\n",
      "   0.81940231  0.90604189  0.85113747  0.92232909  0.95446468  0.96807585\n",
      "   1.          0.97544936  0.94929819  0.90101623]\n",
      " [ 0.43354042  0.44434138  0.42284466  0.57151805  0.60606285  0.61207149\n",
      "   0.66322314  0.68705033  0.75592771  0.68660649  0.67263487  0.67446215\n",
      "   0.78908987  0.89315103  0.83666779  0.90586091  0.93973293  0.95342828\n",
      "   0.97544936  1.          0.95480226  0.88682485]\n",
      " [ 0.40078591  0.41321994  0.39009344  0.53523694  0.57425263  0.57542714\n",
      "   0.63891663  0.67618245  0.75873364  0.67403901  0.65901864  0.65752698\n",
      "   0.78988243  0.93743026  0.86549926  0.90819783  0.94496354  0.92545923\n",
      "   0.94929819  0.95480226  1.          0.94475224]\n",
      " [ 0.38811857  0.40187722  0.37844776  0.5235819   0.56333804  0.55568258\n",
      "   0.61968253  0.65774621  0.74061758  0.66078264  0.64010961  0.6379262\n",
      "   0.76515913  0.91276605  0.83550572  0.87419709  0.90436667  0.87015031\n",
      "   0.90101623  0.88682485  0.94475224  1.        ]]\n",
      "[[ 0.99206281  0.97524041  0.96563236  0.95451389  0.96873424  0.96760248\n",
      "   0.96342369  0.96112444  0.89354979  0.87874618  0.93393283  0.94045523\n",
      "   0.90290363  0.79939183  0.77542016  0.77329663  0.73017552  0.70230939\n",
      "   0.64605766  0.77422283  0.76104462  0.78017903]\n",
      " [ 1.          0.99083791  0.97751096  0.96757045  0.97590419  0.97536669\n",
      "   0.96811871  0.95674341  0.89149687  0.88032307  0.93032796  0.93494321\n",
      "   0.91021811  0.82358064  0.75406936  0.74741023  0.71637524  0.68535085\n",
      "   0.64016625  0.74952575  0.73304853  0.725308  ]\n",
      " [ 0.99083791  1.          0.99046443  0.98454411  0.97867169  0.97744722\n",
      "   0.96627254  0.94767968  0.88089285  0.86814197  0.91532853  0.91915244\n",
      "   0.90329138  0.83312042  0.72494634  0.71952869  0.689478    0.65683858\n",
      "   0.61019972  0.71340682  0.69413497  0.69148748]\n",
      " [ 0.97751096  0.99046443  1.          0.99449542  0.97671936  0.97231255\n",
      "   0.95960976  0.94971731  0.87306244  0.8632802   0.90148364  0.91854911\n",
      "   0.89636122  0.80712822  0.73194461  0.73920815  0.70252248  0.6621904\n",
      "   0.59766687  0.71938577  0.70905873  0.73670866]\n",
      " [ 0.96757045  0.98454411  0.99449542  1.          0.98236162  0.97871995\n",
      "   0.96546886  0.95011054  0.87351008  0.86694892  0.89931928  0.91428511\n",
      "   0.89258862  0.80837876  0.71923825  0.72794914  0.6926632   0.65115788\n",
      "   0.58464871  0.70743896  0.70187762  0.72257241]\n",
      " [ 0.97590419  0.97867169  0.97671936  0.98236162  1.          0.99221169\n",
      "   0.9865605   0.96198457  0.88147424  0.87237415  0.90857751  0.92231857\n",
      "   0.88861644  0.78820445  0.72814934  0.73837815  0.70542317  0.65701156\n",
      "   0.59191429  0.714118    0.70797725  0.72675005]\n",
      " [ 0.97536669  0.97744722  0.97231255  0.97871995  0.99221169  1.\n",
      "   0.98881929  0.97259605  0.89917032  0.89066329  0.93027241  0.93792616\n",
      "   0.90606097  0.81651986  0.75205801  0.7553944   0.72333848  0.68656332\n",
      "   0.6319218   0.74767266  0.7341792   0.73621047]\n",
      " [ 0.96811871  0.96627254  0.95960976  0.96546886  0.9865605   0.98881929\n",
      "   1.          0.97613161  0.90686003  0.88757255  0.92982392  0.93756039\n",
      "   0.91090652  0.82567717  0.73972863  0.73610941  0.70816582  0.67770147\n",
      "   0.62356349  0.73276099  0.71825536  0.70524366]\n",
      " [ 0.95674341  0.94767968  0.94971731  0.95011054  0.96198457  0.97259605\n",
      "   0.97613161  1.          0.93687351  0.92348312  0.95989949  0.96793016\n",
      "   0.93994261  0.83521387  0.79951829  0.78834063  0.75833419  0.74742532\n",
      "   0.69197537  0.81116257  0.80028574  0.7763264 ]\n",
      " [ 0.89149687  0.88089285  0.87306244  0.87351008  0.88147424  0.89917032\n",
      "   0.90686003  0.93687351  1.          0.97920659  0.92969283  0.91990068\n",
      "   0.90956446  0.83668859  0.75095686  0.71875696  0.70520998  0.71882818\n",
      "   0.6882089   0.77767858  0.75902503  0.6775302 ]\n",
      " [ 0.88032307  0.86814197  0.8632802   0.86694892  0.87237415  0.89066329\n",
      "   0.88757255  0.92348312  0.97920659  1.          0.94071502  0.93626205\n",
      "   0.92813649  0.85861261  0.76593677  0.72533868  0.72907789  0.73219386\n",
      "   0.71813463  0.80091126  0.80176211  0.67649192]\n",
      " [ 0.93032796  0.91532853  0.90148364  0.89931928  0.90857751  0.93027241\n",
      "   0.92982392  0.95989949  0.92969283  0.94071502  1.          0.98682679\n",
      "   0.97058699  0.90363377  0.80309295  0.75817909  0.75382146  0.76606519\n",
      "   0.74337518  0.82980908  0.81437405  0.70569288]\n",
      " [ 0.93494321  0.91915244  0.91854911  0.91428511  0.92231857  0.93792616\n",
      "   0.93756039  0.96793016  0.91990068  0.93626205  0.98682679  1.\n",
      "   0.97168078  0.88005871  0.8272358   0.7984934   0.78831205  0.78628711\n",
      "   0.74711738  0.84842438  0.83910119  0.75363249]\n",
      " [ 0.91021811  0.90329138  0.89636122  0.89258862  0.88861644  0.90606097\n",
      "   0.91090652  0.93994261  0.90956446  0.92813649  0.97058699  0.97168078\n",
      "   1.          0.92941763  0.78166162  0.73139115  0.74228053  0.75529404\n",
      "   0.74342929  0.80926387  0.78784609  0.6490877 ]\n",
      " [ 0.82358064  0.83312042  0.80712822  0.80837876  0.78820445  0.81651986\n",
      "   0.82567717  0.83521387  0.83668859  0.85861261  0.90363377  0.88005871\n",
      "   0.92941763  1.          0.68677202  0.60192363  0.62230485  0.66716799\n",
      "   0.70368064  0.71362773  0.66661631  0.44654564]\n",
      " [ 0.75406936  0.72494634  0.73194461  0.71923825  0.72814934  0.75205801\n",
      "   0.73972863  0.79951829  0.75095686  0.76593677  0.80309295  0.8272358\n",
      "   0.78166162  0.68677202  1.          0.97067675  0.95756505  0.93805112\n",
      "   0.88176658  0.9311798   0.8090696   0.7750565 ]\n",
      " [ 0.74741023  0.71952869  0.73920815  0.72794914  0.73837815  0.7553944\n",
      "   0.73610941  0.78834063  0.71875696  0.72533868  0.75817909  0.7984934\n",
      "   0.73139115  0.60192363  0.97067675  1.          0.97189436  0.92756102\n",
      "   0.84000135  0.91466931  0.78870069  0.83338485]\n",
      " [ 0.71637524  0.689478    0.70252248  0.6926632   0.70542317  0.72333848\n",
      "   0.70816582  0.75833419  0.70520998  0.72907789  0.75382146  0.78831205\n",
      "   0.74228053  0.62230485  0.95756505  0.97189436  1.          0.95307072\n",
      "   0.89469114  0.91980455  0.78664419  0.74678083]\n",
      " [ 0.68535085  0.65683858  0.6621904   0.65115788  0.65701156  0.68656332\n",
      "   0.67770147  0.74742532  0.71882818  0.73219386  0.76606519  0.78628711\n",
      "   0.75529404  0.66716799  0.93805112  0.92756102  0.95307072  1.\n",
      "   0.94190071  0.9515331   0.81152422  0.69450628]\n",
      " [ 0.64016625  0.61019972  0.59766687  0.58464871  0.59191429  0.6319218\n",
      "   0.62356349  0.69197537  0.6882089   0.71813463  0.74337518  0.74711738\n",
      "   0.74342929  0.70368064  0.88176658  0.84000135  0.89469114  0.94190071\n",
      "   1.          0.94272283  0.79428951  0.56810761]\n",
      " [ 0.74952575  0.71340682  0.71938577  0.70743896  0.714118    0.74767266\n",
      "   0.73276099  0.81116257  0.77767858  0.80091126  0.82980908  0.84842438\n",
      "   0.80926387  0.71362773  0.9311798   0.91466931  0.91980455  0.9515331\n",
      "   0.94272283  1.          0.90972412  0.78162457]\n",
      " [ 0.73304853  0.69413497  0.70905873  0.70187762  0.70797725  0.7341792\n",
      "   0.71825536  0.80028574  0.75902503  0.80176211  0.81437405  0.83910119\n",
      "   0.78784609  0.66661631  0.8090696   0.78870069  0.78664419  0.81152422\n",
      "   0.79428951  0.90972412  1.          0.79648673]\n",
      " [ 0.725308    0.69148748  0.73670866  0.72257241  0.72675005  0.73621047\n",
      "   0.70524366  0.7763264   0.6775302   0.67649192  0.70569288  0.75363249\n",
      "   0.6490877   0.44654564  0.7750565   0.83338485  0.74678083  0.69450628\n",
      "   0.56810761  0.78162457  0.79648673  1.        ]]\n",
      "[[ 0.97737031  0.96736316  0.97441233  0.95700786  0.94064736  0.93258992\n",
      "   0.94467625  0.92434924  0.90418445  0.84907746  0.89508496  0.84545626\n",
      "   0.88287258  0.7305709   0.70409228  0.70513405  0.70670734  0.67438677\n",
      "   0.65002202  0.65020928  0.66902046  0.08140742]\n",
      " [ 1.          0.99027845  0.98767655  0.97292048  0.95307585  0.94884019\n",
      "   0.95786321  0.93129236  0.90832675  0.85381017  0.89704374  0.85355271\n",
      "   0.86923353  0.73645313  0.67970876  0.68441719  0.68622332  0.64958944\n",
      "   0.62696865  0.62797425  0.6403987   0.0764025 ]\n",
      " [ 0.99027845  1.          0.99126799  0.98178202  0.96511754  0.95988058\n",
      "   0.96506574  0.93016054  0.90131827  0.84786178  0.88925842  0.84711653\n",
      "   0.85915172  0.72387027  0.65547043  0.65940289  0.66627282  0.62557067\n",
      "   0.60297453  0.60606517  0.62572391  0.07618348]\n",
      " [ 0.98767655  0.99126799  1.          0.98740621  0.97185207  0.9654084\n",
      "   0.96967789  0.93995194  0.91126165  0.86345908  0.90764104  0.86936559\n",
      "   0.88749901  0.74978753  0.69362441  0.69742692  0.69880306  0.65771074\n",
      "   0.63333818  0.63685605  0.65668984  0.08097436]\n",
      " [ 0.97292048  0.98178202  0.98740621  1.          0.98030795  0.97430125\n",
      "   0.97540419  0.94043331  0.90522332  0.87074206  0.91355791  0.88034362\n",
      "   0.89566791  0.75684139  0.67358676  0.67813724  0.68345582  0.6453346\n",
      "   0.62336681  0.62446229  0.64836258  0.08011427]\n",
      " [ 0.95307585  0.96511754  0.97185207  0.98030795  1.          0.994462\n",
      "   0.98630894  0.96362865  0.9349136   0.91300894  0.93925378  0.90972408\n",
      "   0.90531201  0.78030426  0.71064819  0.71146602  0.71156242  0.67242325\n",
      "   0.6522001   0.65762009  0.68465202  0.08118855]\n",
      " [ 0.94884019  0.95988058  0.9654084   0.97430125  0.994462    1.\n",
      "   0.99103893  0.96801075  0.94279098  0.9230043   0.94301204  0.91737341\n",
      "   0.90481934  0.78891681  0.70434487  0.70888382  0.71085023  0.67178417\n",
      "   0.65461745  0.66338779  0.68404889  0.0786763 ]\n",
      " [ 0.95786321  0.96506574  0.96967789  0.97540419  0.98630894  0.99103893\n",
      "   1.          0.9678669   0.94553425  0.92519666  0.9493302   0.92144251\n",
      "   0.91214217  0.80039752  0.70505887  0.71055412  0.71094837  0.67029676\n",
      "   0.65205926  0.65866337  0.67848526  0.07890753]\n",
      " [ 0.93129236  0.93016054  0.93995194  0.94043331  0.96362865  0.96801075\n",
      "   0.9678669   1.          0.97776085  0.94156095  0.95164059  0.9200466\n",
      "   0.92929838  0.84885436  0.79507135  0.7916123   0.7983224   0.76203669\n",
      "   0.73544022  0.74800598  0.77411498  0.09536165]\n",
      " [ 0.90832675  0.90131827  0.91126165  0.90522332  0.9349136   0.94279098\n",
      "   0.94553425  0.97776085  1.          0.96420149  0.95929619  0.91675847\n",
      "   0.91186188  0.83153124  0.76025965  0.75952882  0.76787741  0.73709805\n",
      "   0.7174622   0.73160419  0.75267883  0.09191427]\n",
      " [ 0.85381017  0.84786178  0.86345908  0.87074206  0.91300894  0.9230043\n",
      "   0.92519666  0.94156095  0.96420149  1.          0.97472357  0.94302168\n",
      "   0.90617062  0.83918835  0.72193486  0.72800613  0.72180661  0.68751759\n",
      "   0.67672642  0.68814786  0.70648981  0.10402663]\n",
      " [ 0.89704374  0.88925842  0.90764104  0.91355791  0.93925378  0.94301204\n",
      "   0.9493302   0.95164059  0.95929619  0.97472357  1.          0.96543812\n",
      "   0.94509892  0.85109379  0.75182384  0.75698853  0.74784692  0.71108906\n",
      "   0.69527109  0.70130564  0.72236175  0.11897927]\n",
      " [ 0.85355271  0.84711653  0.86936559  0.88034362  0.90972408  0.91737341\n",
      "   0.92144251  0.9200466   0.91675847  0.94302168  0.96543812  1.\n",
      "   0.95324583  0.89515373  0.78776587  0.79784401  0.78807514  0.7600374\n",
      "   0.74812142  0.74611521  0.75553971  0.11652425]\n",
      " [ 0.86923353  0.85915172  0.88749901  0.89566791  0.90531201  0.90481934\n",
      "   0.91214217  0.92929838  0.91186188  0.90617062  0.94509892  0.95324583\n",
      "   1.          0.90949729  0.8234004   0.82689182  0.82491853  0.79227312\n",
      "   0.7653428   0.77246097  0.80253068  0.10239627]\n",
      " [ 0.73645313  0.72387027  0.74978753  0.75684139  0.78030426  0.78891681\n",
      "   0.80039752  0.84885436  0.83153124  0.83918835  0.85109379  0.89515373\n",
      "   0.90949729  1.          0.86827501  0.87327551  0.87519595  0.84827916\n",
      "   0.82108986  0.82709488  0.84421375  0.09446282]\n",
      " [ 0.67970876  0.65547043  0.69362441  0.67358676  0.71064819  0.70434487\n",
      "   0.70505887  0.79507135  0.76025965  0.72193486  0.75182384  0.78776587\n",
      "   0.8234004   0.86827501  1.          0.99289036  0.98395151  0.96907643\n",
      "   0.94859052  0.94154978  0.9467387   0.10342029]\n",
      " [ 0.68441719  0.65940289  0.69742692  0.67813724  0.71146602  0.70888382\n",
      "   0.71055412  0.7916123   0.75952882  0.72800613  0.75698853  0.79784401\n",
      "   0.82689182  0.87327551  0.99289036  1.          0.97867869  0.96193076\n",
      "   0.95111581  0.94168481  0.93722501  0.10093316]\n",
      " [ 0.68622332  0.66627282  0.69880306  0.68345582  0.71156242  0.71085023\n",
      "   0.71094837  0.7983224   0.76787741  0.72180661  0.74784692  0.78807514\n",
      "   0.82491853  0.87519595  0.98395151  0.97867869  1.          0.98929437\n",
      "   0.97165511  0.97110626  0.97043052  0.10516662]\n",
      " [ 0.64958944  0.62557067  0.65771074  0.6453346   0.67242325  0.67178417\n",
      "   0.67029676  0.76203669  0.73709805  0.68751759  0.71108906  0.7600374\n",
      "   0.79227312  0.84827916  0.96907643  0.96193076  0.98929437  1.\n",
      "   0.98439969  0.97317611  0.96822299  0.10363473]\n",
      " [ 0.62696865  0.60297453  0.63333818  0.62336681  0.6522001   0.65461745\n",
      "   0.65205926  0.73544022  0.7174622   0.67672642  0.69527109  0.74812142\n",
      "   0.7653428   0.82108986  0.94859052  0.95111581  0.97165511  0.98439969\n",
      "   1.          0.98292923  0.95904067  0.10082311]\n",
      " [ 0.62797425  0.60606517  0.63685605  0.62446229  0.65762009  0.66338779\n",
      "   0.65866337  0.74800598  0.73160419  0.68814786  0.70130564  0.74611521\n",
      "   0.77246097  0.82709488  0.94154978  0.94168481  0.97110626  0.97317611\n",
      "   0.98292923  1.          0.97061173  0.10102284]\n",
      " [ 0.6403987   0.62572391  0.65668984  0.64836258  0.68465202  0.68404889\n",
      "   0.67848526  0.77411498  0.75267883  0.70648981  0.72236175  0.75553971\n",
      "   0.80253068  0.84421375  0.9467387   0.93722501  0.97043052  0.96822299\n",
      "   0.95904067  0.97061173  1.          0.12309637]\n",
      " [ 0.0764025   0.07618348  0.08097436  0.08011427  0.08118855  0.0786763\n",
      "   0.07890753  0.09536165  0.09191427  0.10402663  0.11897927  0.11652425\n",
      "   0.10239627  0.09446282  0.10342029  0.10093316  0.10516662  0.10363473\n",
      "   0.10082311  0.10102284  0.12309637  1.        ]]\n",
      "[[ 0.99411493  0.98044796  0.97535099  0.96485812  0.9730021   0.96083504\n",
      "   0.97352526  0.96782206  0.9697485   0.93463126  0.92772592  0.95676173\n",
      "   0.95400647  0.89781534  0.70172328  0.72261287  0.71851122  0.68894298\n",
      "   0.68541186  0.67092059  0.64453384  0.6132567 ]\n",
      " [ 1.          0.98662126  0.97969849  0.97135222  0.98524878  0.96694369\n",
      "   0.97946318  0.97282363  0.96962942  0.94272242  0.93918315  0.96411647\n",
      "   0.95727243  0.89320801  0.71478044  0.73681473  0.72802587  0.70717083\n",
      "   0.70091013  0.6810938   0.6541787   0.61596098]\n",
      " [ 0.98662126  1.          0.99664919  0.99425144  0.98542962  0.99065581\n",
      "   0.99197739  0.98675589  0.96889998  0.92289843  0.91259369  0.94880971\n",
      "   0.95706244  0.91209356  0.67383294  0.69559178  0.6848932   0.68160848\n",
      "   0.67768014  0.65804093  0.63298644  0.59413742]\n",
      " [ 0.97969849  0.99664919  1.          0.99730482  0.98260057  0.99151992\n",
      "   0.99009021  0.98189774  0.95655163  0.90732619  0.89482371  0.93354573\n",
      "   0.9423199   0.90017283  0.66958852  0.69014584  0.67536876  0.65931209\n",
      "   0.65306113  0.62891452  0.60353165  0.58984657]\n",
      " [ 0.97135222  0.99425144  0.99730482  1.          0.97959451  0.99327046\n",
      "   0.98737968  0.97870187  0.94865572  0.89555372  0.88445146  0.92605492\n",
      "   0.93559557  0.89472528  0.66318369  0.68555492  0.66700399  0.65466816\n",
      "   0.64610706  0.62145434  0.59560254  0.58376361]\n",
      " [ 0.98524878  0.98542962  0.98260057  0.97959451  1.          0.98320888\n",
      "   0.98674374  0.97670401  0.95761504  0.93169515  0.93055955  0.94886217\n",
      "   0.94348328  0.8799015   0.72437195  0.74845293  0.7306941   0.71079863\n",
      "   0.69859206  0.66894192  0.63810471  0.63867852]\n",
      " [ 0.96694369  0.99065581  0.99151992  0.99327046  0.98320888  1.\n",
      "   0.99239987  0.98423052  0.95344667  0.90339551  0.89111485  0.92808025\n",
      "   0.93945798  0.89875071  0.65003755  0.67358776  0.65820721  0.65988271\n",
      "   0.65337764  0.6346183   0.61065628  0.59873981]\n",
      " [ 0.97946318  0.99197739  0.99009021  0.98737968  0.98674374  0.99239987\n",
      "   1.          0.99235197  0.96734225  0.92831916  0.92191047  0.95094967\n",
      "   0.95658539  0.9053267   0.65668829  0.67754296  0.66687931  0.66260794\n",
      "   0.65958885  0.64120193  0.61534994  0.5795945 ]\n",
      " [ 0.97282363  0.98675589  0.98189774  0.97870187  0.97670401  0.98423052\n",
      "   0.99235197  1.          0.97985926  0.94267653  0.9345915   0.95887054\n",
      "   0.96725512  0.91467744  0.65961692  0.67964848  0.67624349  0.68809016\n",
      "   0.68927797  0.67312311  0.64950356  0.60287124]\n",
      " [ 0.96962942  0.96889998  0.95655163  0.94865572  0.95761504  0.95344667\n",
      "   0.96734225  0.97985926  1.          0.97254878  0.96070466  0.97788611\n",
      "   0.98694157  0.93854298  0.70781808  0.72513116  0.74094124  0.76583766\n",
      "   0.77345413  0.76731092  0.74777993  0.65470864]\n",
      " [ 0.94272242  0.92289843  0.90732619  0.89555372  0.93169515  0.90339551\n",
      "   0.92831916  0.94267653  0.97254878  1.          0.98435664  0.97515418\n",
      "   0.96611804  0.88162493  0.73166457  0.75207135  0.77162281  0.7960571\n",
      "   0.80065772  0.79082827  0.7657445   0.68992545]\n",
      " [ 0.93918315  0.91259369  0.89482371  0.88445146  0.93055955  0.89111485\n",
      "   0.92191047  0.9345915   0.96070466  0.98435664  1.          0.97836567\n",
      "   0.96302215  0.87900031  0.76203398  0.77803832  0.79860344  0.80936754\n",
      "   0.81276669  0.79765152  0.77543742  0.65037379]\n",
      " [ 0.96411647  0.94880971  0.93354573  0.92605492  0.94886217  0.92808025\n",
      "   0.95094967  0.95887054  0.97788611  0.97515418  0.97836567  1.\n",
      "   0.99032229  0.90465628  0.70081115  0.71967284  0.73298523  0.75514046\n",
      "   0.76248025  0.76351138  0.73461021  0.62284323]\n",
      " [ 0.95727243  0.95706244  0.9423199   0.93559557  0.94348328  0.93945798\n",
      "   0.95658539  0.96725512  0.98694157  0.96611804  0.96302215  0.99032229\n",
      "   1.          0.94024856  0.66802686  0.68236847  0.70387829  0.74215221\n",
      "   0.75590267  0.7625388   0.74431442  0.5994018 ]\n",
      " [ 0.89320801  0.91209356  0.90017283  0.89472528  0.8799015   0.89875071\n",
      "   0.9053267   0.91467744  0.93854298  0.88162493  0.87900031  0.90465628\n",
      "   0.94024856  1.          0.6823768   0.67475197  0.70063499  0.72999075\n",
      "   0.74877584  0.7581115   0.77172998  0.53714   ]\n",
      " [ 0.71478044  0.67383294  0.66958852  0.66318369  0.72437195  0.65003755\n",
      "   0.65668829  0.65961692  0.70781808  0.73166457  0.76203398  0.70081115\n",
      "   0.66802686  0.6823768   1.          0.99221032  0.98320165  0.89473365\n",
      "   0.86496763  0.79009617  0.78360195  0.76931212]\n",
      " [ 0.73681473  0.69559178  0.69014584  0.68555492  0.74845293  0.67358776\n",
      "   0.67754296  0.67964848  0.72513116  0.75207135  0.77803832  0.71967284\n",
      "   0.68236847  0.67475197  0.99221032  1.          0.98708254  0.90924508\n",
      "   0.87404453  0.79893101  0.78091509  0.80731406]\n",
      " [ 0.72802587  0.6848932   0.67536876  0.66700399  0.7306941   0.65820721\n",
      "   0.66687931  0.67624349  0.74094124  0.77162281  0.79860344  0.73298523\n",
      "   0.70387829  0.70063499  0.98320165  0.98708254  1.          0.94141218\n",
      "   0.91851534  0.85629476  0.84463025  0.7974937 ]\n",
      " [ 0.70717083  0.68160848  0.65931209  0.65466816  0.71079863  0.65988271\n",
      "   0.66260794  0.68809016  0.76583766  0.7960571   0.80936754  0.75514046\n",
      "   0.74215221  0.72999075  0.89473365  0.90924508  0.94141218  1.\n",
      "   0.99378805  0.95906339  0.94518291  0.82427106]\n",
      " [ 0.70091013  0.67768014  0.65306113  0.64610706  0.69859206  0.65337764\n",
      "   0.65958885  0.68927797  0.77345413  0.80065772  0.81276669  0.76248025\n",
      "   0.75590267  0.74877584  0.86496763  0.87404453  0.91851534  0.99378805\n",
      "   1.          0.97633164  0.96433681  0.79415498]\n",
      " [ 0.6810938   0.65804093  0.62891452  0.62145434  0.66894192  0.6346183\n",
      "   0.64120193  0.67312311  0.76731092  0.79082827  0.79765152  0.76351138\n",
      "   0.7625388   0.7581115   0.79009617  0.79893101  0.85629476  0.95906339\n",
      "   0.97633164  1.          0.97213611  0.75877888]\n",
      " [ 0.6541787   0.63298644  0.60353165  0.59560254  0.63810471  0.61065628\n",
      "   0.61534994  0.64950356  0.74777993  0.7657445   0.77543742  0.73461021\n",
      "   0.74431442  0.77172998  0.78360195  0.78091509  0.84463025  0.94518291\n",
      "   0.96433681  0.97213611  1.          0.70925565]\n",
      " [ 0.61596098  0.59413742  0.58984657  0.58376361  0.63867852  0.59873981\n",
      "   0.5795945   0.60287124  0.65470864  0.68992545  0.65037379  0.62284323\n",
      "   0.5994018   0.53714     0.76931212  0.80731406  0.7974937   0.82427106\n",
      "   0.79415498  0.75877888  0.70925565  1.        ]]\n",
      "[[ 0.98117976  0.97231719  0.91182854  0.90954532  0.94108916  0.96748934\n",
      "   0.95672475  0.95447876  0.94633822  0.96668584  0.94487317  0.93778512\n",
      "   0.93212095  0.94782234  0.93059234  0.95567187  0.95553161  0.93525741\n",
      "   0.94026295  0.89842914  0.90981138  0.83525162]\n",
      " [ 1.          0.98213509  0.93820013  0.93818686  0.96274616  0.97254772\n",
      "   0.95039829  0.94246451  0.91213211  0.92977232  0.9133045   0.90577649\n",
      "   0.89761162  0.90425402  0.89594445  0.92109964  0.92266513  0.89580073\n",
      "   0.901123    0.85643507  0.86951565  0.82218445]\n",
      " [ 0.98213509  1.          0.97638558  0.97292233  0.98198423  0.98319832\n",
      "   0.97773877  0.9568336   0.93264429  0.9509987   0.92557914  0.91852412\n",
      "   0.9142688   0.91266866  0.90605953  0.9178595   0.91208655  0.89688036\n",
      "   0.89077433  0.85769882  0.86995809  0.8145915 ]\n",
      " [ 0.93820013  0.97638558  1.          0.99030939  0.98337352  0.96622269\n",
      "   0.95995075  0.92938401  0.89123877  0.90694529  0.89439515  0.88302369\n",
      "   0.88072202  0.86706526  0.86796386  0.86257447  0.85234702  0.8430362\n",
      "   0.8232905   0.79800369  0.81294873  0.74429717]\n",
      " [ 0.93818686  0.97292233  0.99030939  1.          0.98513728  0.97138624\n",
      "   0.95539008  0.91630204  0.88488789  0.90504018  0.88109472  0.86807725\n",
      "   0.86633153  0.86126693  0.8512741   0.85125967  0.84078097  0.83073793\n",
      "   0.81325324  0.78418885  0.79966717  0.73308944]\n",
      " [ 0.96274616  0.98198423  0.98337352  0.98513728  1.          0.98938505\n",
      "   0.97618417  0.95252251  0.9179898   0.92860754  0.91022362  0.90512183\n",
      "   0.90022459  0.88545287  0.88827054  0.89648922  0.89002979  0.87571607\n",
      "   0.86740794  0.83597542  0.85002605  0.77035206]\n",
      " [ 0.97254772  0.98319832  0.96622269  0.97138624  0.98938505  1.\n",
      "   0.98468082  0.96267446  0.93699557  0.95472468  0.93244182  0.92550769\n",
      "   0.92168109  0.91757506  0.91100813  0.92524222  0.9197111   0.90364365\n",
      "   0.89813749  0.86177327  0.87607665  0.79398776]\n",
      " [ 0.95039829  0.97773877  0.95995075  0.95539008  0.97618417  0.98468082\n",
      "   1.          0.981692    0.96522942  0.97163089  0.9421485   0.94510394\n",
      "   0.94226604  0.91626514  0.92989101  0.93782823  0.92784888  0.92391128\n",
      "   0.91554471  0.89631943  0.90540006  0.82552591]\n",
      " [ 0.94246451  0.9568336   0.92938401  0.91630204  0.95252251  0.96267446\n",
      "   0.981692    1.          0.98535498  0.97111547  0.97212122  0.98102328\n",
      "   0.97420331  0.93883762  0.97183521  0.97541043  0.96927026  0.96910359\n",
      "   0.9634015   0.95420918  0.9608999   0.8744565 ]\n",
      " [ 0.91213211  0.93264429  0.89123877  0.88488789  0.9179898   0.93699557\n",
      "   0.96522942  0.98535498  1.          0.98645598  0.97502887  0.98628209\n",
      "   0.98276708  0.95449544  0.97940987  0.98092931  0.97279292  0.98196143\n",
      "   0.97484788  0.97238055  0.97508649  0.88525077]\n",
      " [ 0.92977232  0.9509987   0.90694529  0.90504018  0.92860754  0.95472468\n",
      "   0.97163089  0.97111547  0.98645598  1.          0.9725634   0.97245676\n",
      "   0.97186851  0.96856091  0.96316733  0.97111338  0.96282726  0.96500456\n",
      "   0.9557761   0.93783304  0.94427541  0.85947325]\n",
      " [ 0.9133045   0.92557914  0.89439515  0.88109472  0.91022362  0.93244182\n",
      "   0.9421485   0.97212122  0.97502887  0.9725634   1.          0.99375352\n",
      "   0.98846737  0.98552246  0.99023419  0.98362503  0.98061574  0.98370424\n",
      "   0.96768476  0.95496915  0.96446604  0.87636617]\n",
      " [ 0.90577649  0.91852412  0.88302369  0.86807725  0.90512183  0.92550769\n",
      "   0.94510394  0.98102328  0.98628209  0.97245676  0.99375352  1.\n",
      "   0.99615955  0.97307458  0.99494961  0.9898985   0.98566401  0.9921615\n",
      "   0.9804632   0.97617777  0.98203467  0.89350936]\n",
      " [ 0.89761162  0.9142688   0.88072202  0.86633153  0.90022459  0.92168109\n",
      "   0.94226604  0.97420331  0.98276708  0.97186851  0.98846737  0.99615955\n",
      "   1.          0.9688254   0.98995411  0.98343238  0.97772883  0.98581901\n",
      "   0.97201163  0.96849973  0.97410088  0.88902653]\n",
      " [ 0.90425402  0.91266866  0.86706526  0.86126693  0.88545287  0.91757506\n",
      "   0.91626514  0.93883762  0.95449544  0.96856091  0.98552246  0.97307458\n",
      "   0.9688254   1.          0.96943718  0.96893051  0.96791739  0.96594417\n",
      "   0.95241696  0.92802801  0.93925388  0.8492251 ]\n",
      " [ 0.89594445  0.90605953  0.86796386  0.8512741   0.88827054  0.91100813\n",
      "   0.92989101  0.97183521  0.97940987  0.96316733  0.99023419  0.99494961\n",
      "   0.98995411  0.96943718  1.          0.99092797  0.98674931  0.99372528\n",
      "   0.9816545   0.97911414  0.98488105  0.90078577]\n",
      " [ 0.92109964  0.9178595   0.86257447  0.85125967  0.89648922  0.92524222\n",
      "   0.93782823  0.97541043  0.98092931  0.97111338  0.98362503  0.9898985\n",
      "   0.98343238  0.96893051  0.99092797  1.          0.99842202  0.99665659\n",
      "   0.99537275  0.98177239  0.98816992  0.90215684]\n",
      " [ 0.92266513  0.91208655  0.85234702  0.84078097  0.89002979  0.9197111\n",
      "   0.92784888  0.96927026  0.97279292  0.96282726  0.98061574  0.98566401\n",
      "   0.97772883  0.96791739  0.98674931  0.99842202  1.          0.99486297\n",
      "   0.99545778  0.97793443  0.98508587  0.90289141]\n",
      " [ 0.89580073  0.89688036  0.8430362   0.83073793  0.87571607  0.90364365\n",
      "   0.92391128  0.96910359  0.98196143  0.96500456  0.98370424  0.9921615\n",
      "   0.98581901  0.96594417  0.99372528  0.99665659  0.99486297  1.\n",
      "   0.99488226  0.98954574  0.99430458  0.90871067]\n",
      " [ 0.901123    0.89077433  0.8232905   0.81325324  0.86740794  0.89813749\n",
      "   0.91554471  0.9634015   0.97484788  0.9557761   0.96768476  0.9804632\n",
      "   0.97201163  0.95241696  0.9816545   0.99537275  0.99545778  0.99488226\n",
      "   1.          0.98998099  0.99373086  0.90639342]\n",
      " [ 0.85643507  0.85769882  0.79800369  0.78418885  0.83597542  0.86177327\n",
      "   0.89631943  0.95420918  0.97238055  0.93783304  0.95496915  0.97617777\n",
      "   0.96849973  0.92802801  0.97911414  0.98177239  0.97793443  0.98954574\n",
      "   0.98998099  1.          0.99822107  0.9143349 ]\n",
      " [ 0.86951565  0.86995809  0.81294873  0.79966717  0.85002605  0.87607665\n",
      "   0.90540006  0.9608999   0.97508649  0.94427541  0.96446604  0.98203467\n",
      "   0.97410088  0.93925388  0.98488105  0.98816992  0.98508587  0.99430458\n",
      "   0.99373086  0.99822107  1.          0.91282673]\n",
      " [ 0.82218445  0.8145915   0.74429717  0.73308944  0.77035206  0.79398776\n",
      "   0.82552591  0.8744565   0.88525077  0.85947325  0.87636617  0.89350936\n",
      "   0.88902653  0.8492251   0.90078577  0.90215684  0.90289141  0.90871067\n",
      "   0.90639342  0.9143349   0.91282673  1.        ]]\n",
      "[[ 0.98458442  0.95932914  0.94714236  0.92079538  0.87955196  0.82211104\n",
      "   0.80628955  0.81494517  0.78251861  0.75264527  0.70614418  0.67464448\n",
      "   0.68097989  0.70187003]\n",
      " [ 1.          0.97028359  0.95628406  0.93169251  0.89740863  0.85286336\n",
      "   0.84133073  0.85114579  0.81893647  0.79209262  0.74288391  0.71708977\n",
      "   0.71330231  0.7315262 ]\n",
      " [ 0.97028359  1.          0.98875229  0.93765607  0.9183013   0.85781595\n",
      "   0.83308843  0.8319526   0.79079975  0.77446354  0.70488181  0.68593164\n",
      "   0.67760277  0.69261849]\n",
      " [ 0.95628406  0.98875229  1.          0.94437289  0.92610233  0.86151055\n",
      "   0.8325253   0.82968653  0.77469848  0.75799439  0.67675883  0.65067908\n",
      "   0.65445458  0.6669769 ]\n",
      " [ 0.93169251  0.93765607  0.94437289  1.          0.97889182  0.93145726\n",
      "   0.90966337  0.89831196  0.83811015  0.80551323  0.74416677  0.70967568\n",
      "   0.71553282  0.71717859]\n",
      " [ 0.89740863  0.9183013   0.92610233  0.97889182  1.          0.95917065\n",
      "   0.93263784  0.91038562  0.84506256  0.81546086  0.75435137  0.72282263\n",
      "   0.71868244  0.7105414 ]\n",
      " [ 0.85286336  0.85781595  0.86151055  0.93145726  0.95917065  1.\n",
      "   0.97881513  0.95573687  0.88298807  0.85951319  0.79604777  0.75160527\n",
      "   0.73626975  0.71084293]\n",
      " [ 0.84133073  0.83308843  0.8325253   0.90966337  0.93263784  0.97881513\n",
      "   1.          0.96839011  0.90181038  0.86878132  0.81727515  0.7730569\n",
      "   0.74387709  0.71323097]\n",
      " [ 0.85114579  0.8319526   0.82968653  0.89831196  0.91038562  0.95573687\n",
      "   0.96839011  1.          0.94085257  0.91000452  0.84969104  0.80426124\n",
      "   0.78158836  0.74427822]\n",
      " [ 0.81893647  0.79079975  0.77469848  0.83811015  0.84506256  0.88298807\n",
      "   0.90181038  0.94085257  1.          0.97781588  0.9520709   0.91390887\n",
      "   0.87113755  0.83327982]\n",
      " [ 0.79209262  0.77446354  0.75799439  0.80551323  0.81546086  0.85951319\n",
      "   0.86878132  0.91000452  0.97781588  1.          0.95740288  0.91389614\n",
      "   0.87474578  0.83158933]\n",
      " [ 0.74288391  0.70488181  0.67675883  0.74416677  0.75435137  0.79604777\n",
      "   0.81727515  0.84969104  0.9520709   0.95740288  1.          0.95772989\n",
      "   0.91143455  0.86732481]\n",
      " [ 0.71708977  0.68593164  0.65067908  0.70967568  0.72282263  0.75160527\n",
      "   0.7730569   0.80426124  0.91390887  0.91389614  0.95772989  1.\n",
      "   0.94280243  0.90138694]\n",
      " [ 0.71330231  0.67760277  0.65445458  0.71553282  0.71868244  0.73626975\n",
      "   0.74387709  0.78158836  0.87113755  0.87474578  0.91143455  0.94280243\n",
      "   1.          0.96191   ]\n",
      " [ 0.7315262   0.69261849  0.6669769   0.71717859  0.7105414   0.71084293\n",
      "   0.71323097  0.74427822  0.83327982  0.83158933  0.86732481  0.90138694\n",
      "   0.96191     1.        ]]\n",
      "[[ 0.99875696  0.99856137  0.99884663  0.30419478  0.31393059  0.2986028\n",
      "   0.29773017  0.31014546  0.3040722   0.30018742  0.30498535  0.29488601\n",
      "   0.29186227  0.26951008]\n",
      " [ 1.          0.99974242  0.99923386  0.27720643  0.28930613  0.26926677\n",
      "   0.26862459  0.28245751  0.27331012  0.27870014  0.28558784  0.27445235\n",
      "   0.27220356  0.2568801 ]\n",
      " [ 0.99974242  1.          0.99951816  0.27228472  0.28408139  0.26364951\n",
      "   0.26302182  0.27637444  0.26732096  0.27179751  0.27891139  0.26748566\n",
      "   0.26598449  0.25202332]\n",
      " [ 0.99923386  0.99951816  1.          0.28503388  0.29496935  0.27611928\n",
      "   0.27637547  0.28906127  0.28176618  0.28072905  0.2871075   0.27601051\n",
      "   0.27433209  0.2576774 ]\n",
      " [ 0.27720643  0.27228472  0.28503388  1.          0.98885912  0.98121823\n",
      "   0.9881365   0.9842065   0.97375925  0.96587842  0.96248848  0.96069394\n",
      "   0.96263468  0.89855487]\n",
      " [ 0.28930613  0.28408139  0.29496935  0.98885912  1.          0.98312798\n",
      "   0.97825177  0.97607548  0.96157182  0.97928626  0.97486861  0.97072785\n",
      "   0.97226107  0.92203572]\n",
      " [ 0.26926677  0.26364951  0.27611928  0.98121823  0.98312798  1.\n",
      "   0.99197984  0.98449152  0.97957993  0.96098645  0.94820424  0.94975912\n",
      "   0.94532812  0.8622604 ]\n",
      " [ 0.26862459  0.26302182  0.27637547  0.9881365   0.97825177  0.99197984\n",
      "   1.          0.99497883  0.98976659  0.96255374  0.95344804  0.9538482\n",
      "   0.94901828  0.86297718]\n",
      " [ 0.28245751  0.27637444  0.28906127  0.9842065   0.97607548  0.98449152\n",
      "   0.99497883  1.          0.98551666  0.97288572  0.96430845  0.96256812\n",
      "   0.95816866  0.87818456]\n",
      " [ 0.27331012  0.26732096  0.28176618  0.97375925  0.96157182  0.97957993\n",
      "   0.98976659  0.98551666  1.          0.94950801  0.92967316  0.93035891\n",
      "   0.92020979  0.8244619 ]\n",
      " [ 0.27870014  0.27179751  0.28072905  0.96587842  0.97928626  0.96098645\n",
      "   0.96255374  0.97288572  0.94950801  1.          0.99342264  0.98509697\n",
      "   0.98544242  0.93798601]\n",
      " [ 0.28558784  0.27891139  0.2871075   0.96248848  0.97486861  0.94820424\n",
      "   0.95344804  0.96430845  0.92967316  0.99342264  1.          0.99068347\n",
      "   0.99197481  0.95426536]\n",
      " [ 0.27445235  0.26748566  0.27601051  0.96069394  0.97072785  0.94975912\n",
      "   0.9538482   0.96256812  0.93035891  0.98509697  0.99068347  1.\n",
      "   0.98846033  0.94558689]\n",
      " [ 0.27220356  0.26598449  0.27433209  0.96263468  0.97226107  0.94532812\n",
      "   0.94901828  0.95816866  0.92020979  0.98544242  0.99197481  0.98846033\n",
      "   1.          0.96976987]\n",
      " [ 0.2568801   0.25202332  0.2576774   0.89855487  0.92203572  0.8622604\n",
      "   0.86297718  0.87818456  0.8244619   0.93798601  0.95426536  0.94558689\n",
      "   0.96976987  1.        ]]\n",
      "[[ 0.99678431  0.9913727   0.98970025  0.93962268  0.91836846  0.84051583\n",
      "   0.86976547  0.87937203  0.81995695  0.87358442  0.84574104  0.9028184\n",
      "   0.92619029  0.95139323]\n",
      " [ 1.          0.99810566  0.99725037  0.95688994  0.93803488  0.86731053\n",
      "   0.89513417  0.90642883  0.85058089  0.89875098  0.87466944  0.92840647\n",
      "   0.9456776   0.96466017]\n",
      " [ 0.99810566  1.          0.99938662  0.9664158   0.94965536  0.88496185\n",
      "   0.91074884  0.92316378  0.87047851  0.9145365   0.8918838   0.94132108\n",
      "   0.95538764  0.97193838]\n",
      " [ 0.99725037  0.99938662  1.          0.97174868  0.95624472  0.89397443\n",
      "   0.91882085  0.93005216  0.87953146  0.92213663  0.90013971  0.94609333\n",
      "   0.9607073   0.97524375]\n",
      " [ 0.95688994  0.9664158   0.97174868  1.          0.99767935  0.97208992\n",
      "   0.98384751  0.98432023  0.96185176  0.98184338  0.96916262  0.98178675\n",
      "   0.9918319   0.98889674]\n",
      " [ 0.93803488  0.94965536  0.95624472  0.99767935  1.          0.98363422\n",
      "   0.99163231  0.98989672  0.97475464  0.98869234  0.97750843  0.98133626\n",
      "   0.99005697  0.98307341]\n",
      " [ 0.86731053  0.88496185  0.89397443  0.97208992  0.98363422  1.\n",
      "   0.99673397  0.98814418  0.99370927  0.99079214  0.98384893  0.9642066\n",
      "   0.96584064  0.94567739]\n",
      " [ 0.89513417  0.91074884  0.91882085  0.98384751  0.99163231  0.99673397\n",
      "   1.          0.99462773  0.99186104  0.99467187  0.98827772  0.9781977\n",
      "   0.97913821  0.96204739]\n",
      " [ 0.90642883  0.92316378  0.93005216  0.98432023  0.98989672  0.98814418\n",
      "   0.99462773  1.          0.98921677  0.99407085  0.98898435  0.98664109\n",
      "   0.98203676  0.96736142]\n",
      " [ 0.85058089  0.87047851  0.87953146  0.96185176  0.97475464  0.99370927\n",
      "   0.99186104  0.98921677  1.          0.99234271  0.98933152  0.96808424\n",
      "   0.96332651  0.9377392 ]\n",
      " [ 0.89875098  0.9145365   0.92213663  0.98184338  0.98869234  0.99079214\n",
      "   0.99467187  0.99407085  0.99234271  1.          0.9897743   0.97997791\n",
      "   0.98010739  0.96218523]\n",
      " [ 0.87466944  0.8918838   0.90013971  0.96916262  0.97750843  0.98384893\n",
      "   0.98827772  0.98898435  0.98933152  0.9897743   1.          0.98211509\n",
      "   0.97385105  0.95227756]\n",
      " [ 0.92840647  0.94132108  0.94609333  0.98178675  0.98133626  0.9642066\n",
      "   0.9781977   0.98664109  0.96808424  0.97997791  0.98211509  1.\n",
      "   0.98773823  0.9740248 ]\n",
      " [ 0.9456776   0.95538764  0.9607073   0.9918319   0.99005697  0.96584064\n",
      "   0.97913821  0.98203676  0.96332651  0.98010739  0.97385105  0.98773823\n",
      "   1.          0.99068096]\n",
      " [ 0.96466017  0.97193838  0.97524375  0.98889674  0.98307341  0.94567739\n",
      "   0.96204739  0.96736142  0.9377392   0.96218523  0.95227756  0.9740248\n",
      "   0.99068096  1.        ]]\n",
      "[[ 0.97740211  0.96865003  0.97665312  0.93864293  0.91243023  0.71278214\n",
      "   0.70624258  0.63046913  0.65821392  0.70509126  0.78159473  0.79223805\n",
      "   0.83191203  0.80755169]\n",
      " [ 1.          0.99016022  0.99282175  0.94912324  0.9247501   0.71827787\n",
      "   0.71549666  0.64739988  0.68053069  0.72839979  0.81544298  0.81479349\n",
      "   0.83954962  0.79827115]\n",
      " [ 0.99016022  1.          0.99379171  0.94753891  0.93847136  0.76096692\n",
      "   0.75076478  0.68315412  0.71452048  0.76117659  0.83856607  0.81811065\n",
      "   0.84481692  0.79772392]\n",
      " [ 0.99282175  0.99379171  1.          0.94948446  0.93223686  0.73927598\n",
      "   0.73320582  0.66310574  0.6969799   0.74497228  0.82057312  0.81363558\n",
      "   0.84371805  0.79572324]\n",
      " [ 0.94912324  0.94753891  0.94948446  1.          0.98325899  0.81777544\n",
      "   0.81896618  0.75630837  0.78114729  0.82255499  0.88356832  0.879831\n",
      "   0.8912613   0.86798772]\n",
      " [ 0.9247501   0.93847136  0.93223686  0.98325899  1.          0.88523129\n",
      "   0.87648807  0.81835981  0.84545565  0.87686857  0.90858582  0.86963935\n",
      "   0.88115459  0.86044292]\n",
      " [ 0.71827787  0.76096692  0.73927598  0.81777544  0.88523129  1.\n",
      "   0.99015968  0.97067963  0.96689486  0.93648576  0.8963295   0.77781073\n",
      "   0.7264787   0.74402095]\n",
      " [ 0.71549666  0.75076478  0.73320582  0.81896618  0.87648807  0.99015968\n",
      "   1.          0.98711654  0.97852641  0.9449938   0.90506081  0.79112827\n",
      "   0.72307781  0.74262092]\n",
      " [ 0.64739988  0.68315412  0.66310574  0.75630837  0.81835981  0.97067963\n",
      "   0.98711654  1.          0.98244493  0.94041047  0.88599088  0.7608727\n",
      "   0.67634076  0.70051358]\n",
      " [ 0.68053069  0.71452048  0.6969799   0.78114729  0.84545565  0.96689486\n",
      "   0.97852641  0.98244493  1.          0.96275954  0.90221261  0.76567361\n",
      "   0.69316004  0.69764021]\n",
      " [ 0.72839979  0.76117659  0.74497228  0.82255499  0.87686857  0.93648576\n",
      "   0.9449938   0.94041047  0.96275954  1.          0.95040886  0.83658077\n",
      "   0.7847939   0.76649866]\n",
      " [ 0.81544298  0.83856607  0.82057312  0.88356832  0.90858582  0.8963295\n",
      "   0.90506081  0.88599088  0.90221261  0.95040886  1.          0.92887766\n",
      "   0.8678213   0.8502059 ]\n",
      " [ 0.81479349  0.81811065  0.81363558  0.879831    0.86963935  0.77781073\n",
      "   0.79112827  0.7608727   0.76567361  0.83658077  0.92887766  1.\n",
      "   0.93356954  0.92213651]\n",
      " [ 0.83954962  0.84481692  0.84371805  0.8912613   0.88115459  0.7264787\n",
      "   0.72307781  0.67634076  0.69316004  0.7847939   0.8678213   0.93356954\n",
      "   1.          0.9707725 ]\n",
      " [ 0.79827115  0.79772392  0.79572324  0.86798772  0.86044292  0.74402095\n",
      "   0.74262092  0.70051358  0.69764021  0.76649866  0.8502059   0.92213651\n",
      "   0.9707725   1.        ]]\n",
      "[[ 0.98229835  0.96313809  0.95767016  0.9429903   0.93560371  0.91614227\n",
      "   0.91240252  0.89460189  0.91372549  0.9094828   0.92197383  0.94663006\n",
      "   0.93425139  0.9555739 ]\n",
      " [ 1.          0.98848673  0.98186873  0.94817742  0.93037069  0.90292999\n",
      "   0.90769256  0.90429076  0.91477239  0.91286483  0.92374479  0.91691333\n",
      "   0.90413409  0.93220124]\n",
      " [ 0.98848673  1.          0.99627295  0.96105129  0.94560133  0.91429502\n",
      "   0.92262779  0.92254175  0.9325127   0.92762946  0.93703063  0.922845\n",
      "   0.90301478  0.91833448]\n",
      " [ 0.98186873  0.99627295  1.          0.97435009  0.9589984   0.92919232\n",
      "   0.93839449  0.93906328  0.94762565  0.94223684  0.9501865   0.93245779\n",
      "   0.91401351  0.92458206]\n",
      " [ 0.94817742  0.96105129  0.97435009  1.          0.98982093  0.96286556\n",
      "   0.97006652  0.97214664  0.97518396  0.97066477  0.98154101  0.95461012\n",
      "   0.94897884  0.95778346]\n",
      " [ 0.93037069  0.94560133  0.9589984   0.98982093  1.          0.98730747\n",
      "   0.98901478  0.98241166  0.98315257  0.98459166  0.9820736   0.97418066\n",
      "   0.96358521  0.95849967]\n",
      " [ 0.90292999  0.91429502  0.92919232  0.96286556  0.98730747  1.\n",
      "   0.99410215  0.98158129  0.97390613  0.98217873  0.96408123  0.97352926\n",
      "   0.95447797  0.93926051]\n",
      " [ 0.90769256  0.92262779  0.93839449  0.97006652  0.98901478  0.99410215\n",
      "   1.          0.98815616  0.9830021   0.99078817  0.97844914  0.97159258\n",
      "   0.95187019  0.93726852]\n",
      " [ 0.90429076  0.92254175  0.93906328  0.97214664  0.98241166  0.98158129\n",
      "   0.98815616  1.          0.97896923  0.98714899  0.97995471  0.94945279\n",
      "   0.93357702  0.92380004]\n",
      " [ 0.91477239  0.9325127   0.94762565  0.97518396  0.98315257  0.97390613\n",
      "   0.9830021   0.97896923  1.          0.98721414  0.98239727  0.96754838\n",
      "   0.9513328   0.93848788]\n",
      " [ 0.91286483  0.92762946  0.94223684  0.97066477  0.98459166  0.98217873\n",
      "   0.99078817  0.98714899  0.98721414  1.          0.98760259  0.96752978\n",
      "   0.9513263   0.93781349]\n",
      " [ 0.92374479  0.93703063  0.9501865   0.98154101  0.9820736   0.96408123\n",
      "   0.97844914  0.97995471  0.98239727  0.98760259  1.          0.96478671\n",
      "   0.94871625  0.94758908]\n",
      " [ 0.91691333  0.922845    0.93245779  0.95461012  0.97418066  0.97352926\n",
      "   0.97159258  0.94945279  0.96754838  0.96752978  0.96478671  1.\n",
      "   0.97918501  0.95870916]\n",
      " [ 0.90413409  0.90301478  0.91401351  0.94897884  0.96358521  0.95447797\n",
      "   0.95187019  0.93357702  0.9513328   0.9513263   0.94871625  0.97918501\n",
      "   1.          0.98258846]\n",
      " [ 0.93220124  0.91833448  0.92458206  0.95778346  0.95849967  0.93926051\n",
      "   0.93726852  0.92380004  0.93848788  0.93781349  0.94758908  0.95870916\n",
      "   0.98258846  1.        ]]\n",
      "[[ 0.99430238  0.99627191  0.98889121  0.97294264  0.98445007  0.98871386\n",
      "   0.97894025  0.9695719   0.94107828  0.92868973  0.89762686  0.86881676\n",
      "   0.91937795  0.90734483]\n",
      " [ 1.          0.99889198  0.99144747  0.97269308  0.98130542  0.98341365\n",
      "   0.98137314  0.97587073  0.95281547  0.92745039  0.89902837  0.85127616\n",
      "   0.90113663  0.90996345]\n",
      " [ 0.99889198  1.          0.99555651  0.98042325  0.98779159  0.98767396\n",
      "   0.98480086  0.97625907  0.9573663   0.9370373   0.90965694  0.86765159\n",
      "   0.91505199  0.92266821]\n",
      " [ 0.99144747  0.99555651  1.          0.99394436  0.99484127  0.98858803\n",
      "   0.98986828  0.97676784  0.97258266  0.95768108  0.9358663   0.89520049\n",
      "   0.9341197   0.95267401]\n",
      " [ 0.97269308  0.98042325  0.99394436  1.          0.99527255  0.98255759\n",
      "   0.98667547  0.96996296  0.98093404  0.97523782  0.95948698  0.92584022\n",
      "   0.94944956  0.97643825]\n",
      " [ 0.98130542  0.98779159  0.99484127  0.99527255  1.          0.98468744\n",
      "   0.9827719   0.97153381  0.97732097  0.96667046  0.9450062   0.91767092\n",
      "   0.93776564  0.96356465]\n",
      " [ 0.98341365  0.98767396  0.98858803  0.98255759  0.98468744  1.\n",
      "   0.99547843  0.98697805  0.95950576  0.96481954  0.94444962  0.91976141\n",
      "   0.95929517  0.92758252]\n",
      " [ 0.98137314  0.98480086  0.98986828  0.98667547  0.9827719   0.99547843\n",
      "   1.          0.9926014   0.97416541  0.97389086  0.95841712  0.92017617\n",
      "   0.95712405  0.94219394]\n",
      " [ 0.97587073  0.97625907  0.97676784  0.96996296  0.97153381  0.98697805\n",
      "   0.9926014   1.          0.97570219  0.97135613  0.95490807  0.91531501\n",
      "   0.93964427  0.92338162]\n",
      " [ 0.95281547  0.9573663   0.97258266  0.98093404  0.97732097  0.95950576\n",
      "   0.97416541  0.97570219  1.          0.98334112  0.97470807  0.92903649\n",
      "   0.92350858  0.97386775]\n",
      " [ 0.92745039  0.9370373   0.95768108  0.97523782  0.96667046  0.96481954\n",
      "   0.97389086  0.97135613  0.98334112  1.          0.99560655  0.97654314\n",
      "   0.9649899   0.96974227]\n",
      " [ 0.89902837  0.90965694  0.9358663   0.95948698  0.9450062   0.94444962\n",
      "   0.95841712  0.95490807  0.97470807  0.99560655  1.          0.9796626\n",
      "   0.95953769  0.9647961 ]\n",
      " [ 0.85127616  0.86765159  0.89520049  0.92584022  0.91767092  0.91976141\n",
      "   0.92017617  0.91531501  0.92903649  0.97654314  0.9796626   1.\n",
      "   0.9631694   0.93366928]\n",
      " [ 0.90113663  0.91505199  0.9341197   0.94944956  0.93776564  0.95929517\n",
      "   0.95712405  0.93964427  0.92350858  0.9649899   0.95953769  0.9631694\n",
      "   1.          0.928468  ]\n",
      " [ 0.90996345  0.92266821  0.95267401  0.97643825  0.96356465  0.92758252\n",
      "   0.94219394  0.92338162  0.97386775  0.96974227  0.9647961   0.93366928\n",
      "   0.928468    1.        ]]\n",
      "[[ 0.96503142  0.9398386   0.91926253  0.92977546  0.91617777  0.88708456\n",
      "   0.88973779  0.86619783  0.80222673  0.73209437  0.72736624  0.73077369\n",
      "   0.8271251   0.80032299  0.72059345  0.73241581  0.67953328  0.68104548\n",
      "   0.62523346  0.64730294]\n",
      " [ 1.          0.96066841  0.92223127  0.93594378  0.91686628  0.89018004\n",
      "   0.88238407  0.85388772  0.80736668  0.74910479  0.74386561  0.74891813\n",
      "   0.81423755  0.77933633  0.73753574  0.73979513  0.68094675  0.69555445\n",
      "   0.64726065  0.66924326]\n",
      " [ 0.96066841  1.          0.95892399  0.9566566   0.93873732  0.90727178\n",
      "   0.88997348  0.86579294  0.82541733  0.77134492  0.76054678  0.75440369\n",
      "   0.8051402   0.7765074   0.73652591  0.73329224  0.68358985  0.70702602\n",
      "   0.65737285  0.67990982]\n",
      " [ 0.92223127  0.95892399  1.          0.96874262  0.95112698  0.9142077\n",
      "   0.88494358  0.86157025  0.81789563  0.76205587  0.74446507  0.72295543\n",
      "   0.78373793  0.75838115  0.73107463  0.72685787  0.66730691  0.69996885\n",
      "   0.66028164  0.67216153]\n",
      " [ 0.93594378  0.9566566   0.96874262  1.          0.97156728  0.92768739\n",
      "   0.92872614  0.90158845  0.8567493   0.80244959  0.78827194  0.77171603\n",
      "   0.81227654  0.78830429  0.74329611  0.74325551  0.68908492  0.71467402\n",
      "   0.66298766  0.69263268]\n",
      " [ 0.91686628  0.93873732  0.95112698  0.97156728  1.          0.93667043\n",
      "   0.93868006  0.91088514  0.88496054  0.83085486  0.82275424  0.8010167\n",
      "   0.83939114  0.81627302  0.79522842  0.78945996  0.72991055  0.74565306\n",
      "   0.70720149  0.73797253]\n",
      " [ 0.89018004  0.90727178  0.9142077   0.92768739  0.93667043  1.\n",
      "   0.89154546  0.86839586  0.80780482  0.75441621  0.74072897  0.73481219\n",
      "   0.77911191  0.75740457  0.72734067  0.73787408  0.68049651  0.68445626\n",
      "   0.64709304  0.68096037]\n",
      " [ 0.88238407  0.88997348  0.88494358  0.92872614  0.93868006  0.89154546\n",
      "   1.          0.9557164   0.89329007  0.84175031  0.82240763  0.81036169\n",
      "   0.84318031  0.81751748  0.77184738  0.78176525  0.72193555  0.73118706\n",
      "   0.67038451  0.70964262]\n",
      " [ 0.85388772  0.86579294  0.86157025  0.90158845  0.91088514  0.86839586\n",
      "   0.9557164   1.          0.93709089  0.87400472  0.83019796  0.8382261\n",
      "   0.87356999  0.85888369  0.76321337  0.78353264  0.72292276  0.73161944\n",
      "   0.67501502  0.71003266]\n",
      " [ 0.80736668  0.82541733  0.81789563  0.8567493   0.88496054  0.80780482\n",
      "   0.89329007  0.93709089  1.          0.94832853  0.88705168  0.88355915\n",
      "   0.87198748  0.86272633  0.78782923  0.79877648  0.73896494  0.74431999\n",
      "   0.6862753   0.72097286]\n",
      " [ 0.74910479  0.77134492  0.76205587  0.80244959  0.83085486  0.75441621\n",
      "   0.84175031  0.87400472  0.94832853  1.          0.94552337  0.93054631\n",
      "   0.8175433   0.8110231   0.75148347  0.76216305  0.69896763  0.70256552\n",
      "   0.65738246  0.69474892]\n",
      " [ 0.74386561  0.76054678  0.74446507  0.78827194  0.82275424  0.74072897\n",
      "   0.82240763  0.83019796  0.88705168  0.94552337  1.          0.9471027\n",
      "   0.81745572  0.79543818  0.77666872  0.77965716  0.7193461   0.71933974\n",
      "   0.68561521  0.7345346 ]\n",
      " [ 0.74891813  0.75440369  0.72295543  0.77171603  0.8010167   0.73481219\n",
      "   0.81036169  0.8382261   0.88355915  0.93054631  0.9471027   1.\n",
      "   0.84859964  0.82709159  0.74287411  0.76037634  0.6906046   0.67957962\n",
      "   0.6454113   0.69463023]\n",
      " [ 0.81423755  0.8051402   0.78373793  0.81227654  0.83939114  0.77911191\n",
      "   0.84318031  0.87356999  0.87198748  0.8175433   0.81745572  0.84859964\n",
      "   1.          0.94860205  0.827879    0.84327569  0.74772077  0.74362444\n",
      "   0.68048133  0.71597908]\n",
      " [ 0.77933633  0.7765074   0.75838115  0.78830429  0.81627302  0.75740457\n",
      "   0.81751748  0.85888369  0.86272633  0.8110231   0.79543818  0.82709159\n",
      "   0.94860205  1.          0.79449864  0.80261686  0.73447071  0.70431269\n",
      "   0.63414055  0.68394116]\n",
      " [ 0.73753574  0.73652591  0.73107463  0.74329611  0.79522842  0.72734067\n",
      "   0.77184738  0.76321337  0.78782923  0.75148347  0.77666872  0.74287411\n",
      "   0.827879    0.79449864  1.          0.95035057  0.79537022  0.81011096\n",
      "   0.78873444  0.82071796]\n",
      " [ 0.73979513  0.73329224  0.72685787  0.74325551  0.78945996  0.73787408\n",
      "   0.78176525  0.78353264  0.79877648  0.76216305  0.77965716  0.76037634\n",
      "   0.84327569  0.80261686  0.95035057  1.          0.82227978  0.81148909\n",
      "   0.80461292  0.82303076]\n",
      " [ 0.68094675  0.68358985  0.66730691  0.68908492  0.72991055  0.68049651\n",
      "   0.72193555  0.72292276  0.73896494  0.69896763  0.7193461   0.6906046\n",
      "   0.74772077  0.73447071  0.79537022  0.82227978  1.          0.71926198\n",
      "   0.71780198  0.7431846 ]\n",
      " [ 0.69555445  0.70702602  0.69996885  0.71467402  0.74565306  0.68445626\n",
      "   0.73118706  0.73161944  0.74431999  0.70256552  0.71933974  0.67957962\n",
      "   0.74362444  0.70431269  0.81011096  0.81148909  0.71926198  1.\n",
      "   0.91066582  0.84618237]\n",
      " [ 0.64726065  0.65737285  0.66028164  0.66298766  0.70720149  0.64709304\n",
      "   0.67038451  0.67501502  0.6862753   0.65738246  0.68561521  0.6454113\n",
      "   0.68048133  0.63414055  0.78873444  0.80461292  0.71780198  0.91066582\n",
      "   1.          0.91363233]\n",
      " [ 0.66924326  0.67990982  0.67216153  0.69263268  0.73797253  0.68096037\n",
      "   0.70964262  0.71003266  0.72097286  0.69474892  0.7345346   0.69463023\n",
      "   0.71597908  0.68394116  0.82071796  0.82303076  0.7431846   0.84618237\n",
      "   0.91363233  1.        ]]\n",
      "[[ 0.99423806  0.99164006  0.9853747   0.98173053  0.97901204  0.97886757\n",
      "   0.9814111   0.9793906   0.97291794  0.97730697  0.97184433  0.95276877\n",
      "   0.96399634  0.95449625  0.939441    0.94790987  0.95269365  0.88660419\n",
      "   0.94957043  0.95303982]\n",
      " [ 1.          0.99402467  0.98891014  0.98175871  0.98268413  0.97904432\n",
      "   0.97750532  0.97151681  0.96346756  0.97050669  0.96431433  0.94367077\n",
      "   0.95691019  0.9490005   0.93034156  0.94147333  0.9435889   0.88280972\n",
      "   0.94533989  0.94741553]\n",
      " [ 0.99402467  1.          0.99143302  0.98661577  0.98570937  0.98566314\n",
      "   0.98717591  0.98032002  0.97235492  0.97848447  0.97218286  0.95207081\n",
      "   0.96747871  0.96145535  0.94085645  0.94974491  0.95282935  0.89436362\n",
      "   0.95558371  0.95839592]\n",
      " [ 0.98891014  0.99143302  1.          0.99452041  0.99403494  0.98842071\n",
      "   0.98348261  0.97821493  0.96965829  0.97342779  0.96564812  0.94287173\n",
      "   0.95753189  0.95007119  0.92786897  0.93346349  0.9396167   0.89418468\n",
      "   0.95798659  0.96313408]\n",
      " [ 0.98175871  0.98661577  0.99452041  1.          0.99461417  0.99066889\n",
      "   0.98662861  0.98032506  0.97812425  0.98125612  0.96515193  0.93968641\n",
      "   0.95475663  0.94205295  0.92206673  0.93094053  0.93816522  0.8788347\n",
      "   0.95213161  0.96113038]\n",
      " [ 0.98268413  0.98570937  0.99403494  0.99461417  1.          0.99166564\n",
      "   0.9846889   0.97868701  0.97583892  0.97681596  0.96241497  0.93535689\n",
      "   0.94648088  0.93820572  0.91488845  0.92320909  0.93597945  0.87968687\n",
      "   0.95191985  0.95924891]\n",
      " [ 0.97904432  0.98566314  0.98842071  0.99066889  0.99166564  1.\n",
      "   0.99453574  0.98784431  0.98341246  0.98288709  0.97769932  0.95287417\n",
      "   0.96080177  0.95692272  0.93304766  0.9411369   0.95176786  0.89594255\n",
      "   0.96371853  0.96297807]\n",
      " [ 0.97750532  0.98717591  0.98348261  0.98662861  0.9846889   0.99453574\n",
      "   1.          0.99261828  0.98827092  0.98870029  0.98396892  0.9631709\n",
      "   0.97077118  0.96618636  0.94548434  0.95427521  0.95941827  0.89383198\n",
      "   0.9614144   0.96262491]\n",
      " [ 0.97151681  0.98032002  0.97821493  0.98032506  0.97868701  0.98784431\n",
      "   0.99261828  1.          0.99275007  0.98753177  0.98912117  0.96980199\n",
      "   0.97755256  0.97467034  0.95959252  0.96157236  0.96693542  0.90985087\n",
      "   0.96711205  0.9710293 ]\n",
      " [ 0.96346756  0.97235492  0.96965829  0.97812425  0.97583892  0.98341246\n",
      "   0.98827092  0.99275007  1.          0.99302752  0.98228091  0.95758918\n",
      "   0.96278245  0.95440241  0.93993369  0.94643461  0.958371    0.88042133\n",
      "   0.95110367  0.95732763]\n",
      " [ 0.97050669  0.97848447  0.97342779  0.98125612  0.97681596  0.98288709\n",
      "   0.98870029  0.98753177  0.99302752  1.          0.98115083  0.95609779\n",
      "   0.96059774  0.95324727  0.93803046  0.94781771  0.95737415  0.87762302\n",
      "   0.9479636   0.94944353]\n",
      " [ 0.96431433  0.97218286  0.96564812  0.96515193  0.96241497  0.97769932\n",
      "   0.98396892  0.98912117  0.98228091  0.98115083  1.          0.9857888\n",
      "   0.98149836  0.9818182   0.97108262  0.97280443  0.9758563   0.91317495\n",
      "   0.96072776  0.95825615]\n",
      " [ 0.94367077  0.95207081  0.94287173  0.93968641  0.93535689  0.95287417\n",
      "   0.9631709   0.96980199  0.95758918  0.95609779  0.9857888   1.\n",
      "   0.97911036  0.98006433  0.97383058  0.97064519  0.97055572  0.91782154\n",
      "   0.94842833  0.94887537]\n",
      " [ 0.95691019  0.96747871  0.95753189  0.95475663  0.94648088  0.96080177\n",
      "   0.97077118  0.97755256  0.96278245  0.96059774  0.98149836  0.97911036\n",
      "   1.          0.99017614  0.97605195  0.97502988  0.968611    0.91596258\n",
      "   0.95250297  0.95878045]\n",
      " [ 0.9490005   0.96145535  0.95007119  0.94205295  0.93820572  0.95692272\n",
      "   0.96618636  0.97467034  0.95440241  0.95324727  0.9818182   0.98006433\n",
      "   0.99017614  1.          0.9847397   0.9766373   0.97369132  0.93820875\n",
      "   0.96223767  0.96209063]\n",
      " [ 0.93034156  0.94085645  0.92786897  0.92206673  0.91488845  0.93304766\n",
      "   0.94548434  0.95959252  0.93993369  0.93803046  0.97108262  0.97383058\n",
      "   0.97605195  0.9847397   1.          0.98266287  0.97584643  0.93455995\n",
      "   0.9479197   0.95055919]\n",
      " [ 0.94147333  0.94974491  0.93346349  0.93094053  0.92320909  0.9411369\n",
      "   0.95427521  0.96157236  0.94643461  0.94781771  0.97280443  0.97064519\n",
      "   0.97502988  0.9766373   0.98266287  1.          0.97526514  0.91485277\n",
      "   0.94069786  0.93937612]\n",
      " [ 0.9435889   0.95282935  0.9396167   0.93816522  0.93597945  0.95176786\n",
      "   0.95941827  0.96693542  0.958371    0.95737415  0.9758563   0.97055572\n",
      "   0.968611    0.97369132  0.97584643  0.97526514  1.          0.92583352\n",
      "   0.95078383  0.94958483]\n",
      " [ 0.88280972  0.89436362  0.89418468  0.8788347   0.87968687  0.89594255\n",
      "   0.89383198  0.90985087  0.88042133  0.87762302  0.91317495  0.91782154\n",
      "   0.91596258  0.93820875  0.93455995  0.91485277  0.92583352  1.\n",
      "   0.94678993  0.94099882]\n",
      " [ 0.94533989  0.95558371  0.95798659  0.95213161  0.95191985  0.96371853\n",
      "   0.9614144   0.96711205  0.95110367  0.9479636   0.96072776  0.94842833\n",
      "   0.95250297  0.96223767  0.9479197   0.94069786  0.95078383  0.94678993\n",
      "   1.          0.98110581]\n",
      " [ 0.94741553  0.95839592  0.96313408  0.96113038  0.95924891  0.96297807\n",
      "   0.96262491  0.9710293   0.95732763  0.94944353  0.95825615  0.94887537\n",
      "   0.95878045  0.96209063  0.95055919  0.93937612  0.94958483  0.94099882\n",
      "   0.98110581  1.        ]]\n",
      "[[ 0.99512504  0.99247061  0.9859888   0.97950126  0.97742311  0.95951571\n",
      "   0.96725467  0.96457104  0.95538116  0.92508899  0.91607063  0.94797946\n",
      "   0.92390018  0.92829209  0.93895928  0.93009403  0.89222556  0.8617541\n",
      "   0.86529447  0.83483262]\n",
      " [ 1.          0.9964509   0.99273467  0.99006117  0.98736805  0.97074039\n",
      "   0.97007711  0.97241645  0.96690367  0.94653756  0.93784475  0.96328677\n",
      "   0.94048465  0.94001017  0.93913794  0.93517321  0.88926275  0.87371477\n",
      "   0.8746148   0.85069902]\n",
      " [ 0.9964509   1.          0.99670358  0.99147602  0.99108459  0.977847\n",
      "   0.97932366  0.98155054  0.97353848  0.95249161  0.94237106  0.96523476\n",
      "   0.94606995  0.94875742  0.94562382  0.94030616  0.88976774  0.88260779\n",
      "   0.88757574  0.86376039]\n",
      " [ 0.99273467  0.99670358  1.          0.99239402  0.99367546  0.98575081\n",
      "   0.98537828  0.98714597  0.98023171  0.96455354  0.9544589   0.97467687\n",
      "   0.9595807   0.9614698   0.94981671  0.9483138   0.88408324  0.89507369\n",
      "   0.90054569  0.88345752]\n",
      " [ 0.99006117  0.99147602  0.99239402  1.          0.99639403  0.98261772\n",
      "   0.97670397  0.98463151  0.97891996  0.9685552   0.96038266  0.97491705\n",
      "   0.96089711  0.95640621  0.94198429  0.94724637  0.88101963  0.89631462\n",
      "   0.89401819  0.87617553]\n",
      " [ 0.98736805  0.99108459  0.99367546  0.99639403  1.          0.98606641\n",
      "   0.98182143  0.98929499  0.98569513  0.97476165  0.9655397   0.97848483\n",
      "   0.9675499   0.96591407  0.95085356  0.95424661  0.88451153  0.90744003\n",
      "   0.90828728  0.8937297 ]\n",
      " [ 0.97074039  0.977847    0.98575081  0.98261772  0.98606641  1.\n",
      "   0.99208261  0.99147974  0.978108    0.97397995  0.96163608  0.97662159\n",
      "   0.96995876  0.97228623  0.94847856  0.95155666  0.87544437  0.91687195\n",
      "   0.92061093  0.9105225 ]\n",
      " [ 0.97007711  0.97932366  0.98537828  0.97670397  0.98182143  0.99208261\n",
      "   1.          0.99292125  0.97583664  0.95865929  0.942369    0.96408421\n",
      "   0.95867597  0.96450307  0.95153218  0.94782675  0.87670984  0.90770428\n",
      "   0.91087298  0.89665356]\n",
      " [ 0.97241645  0.98155054  0.98714597  0.98463151  0.98929499  0.99147974\n",
      "   0.99292125  1.          0.98615169  0.97335403  0.95926019  0.97387969\n",
      "   0.96792514  0.97294418  0.9553773   0.95818137  0.89352366  0.92205235\n",
      "   0.92280943  0.90724014]\n",
      " [ 0.96690367  0.97353848  0.98023171  0.97891996  0.98569513  0.978108\n",
      "   0.97583664  0.98615169  1.          0.98396357  0.97482885  0.9801738\n",
      "   0.97331691  0.9749868   0.95079685  0.95533995  0.8712513   0.9118584\n",
      "   0.91297763  0.90858828]\n",
      " [ 0.94653756  0.95249161  0.96455354  0.9685552   0.97476165  0.97397995\n",
      "   0.95865929  0.97335403  0.98396357  1.          0.99298311  0.98573699\n",
      "   0.98559359  0.98104028  0.93017981  0.94091054  0.84065769  0.90904718\n",
      "   0.90800375  0.91538566]\n",
      " [ 0.93784475  0.94237106  0.9544589   0.96038266  0.9655397   0.96163608\n",
      "   0.942369    0.95926019  0.97482885  0.99298311  1.          0.98844171\n",
      "   0.98361311  0.97293417  0.92654803  0.9382738   0.83602934  0.90422603\n",
      "   0.90173567  0.91171558]\n",
      " [ 0.96328677  0.96523476  0.97467687  0.97491705  0.97848483  0.97662159\n",
      "   0.96408421  0.97387969  0.9801738   0.98573699  0.98844171  1.\n",
      "   0.98583894  0.97847942  0.95362192  0.95686933  0.87253739  0.91899913\n",
      "   0.91504075  0.91435642]\n",
      " [ 0.94048465  0.94606995  0.9595807   0.96089711  0.9675499   0.96995876\n",
      "   0.95867597  0.96792514  0.97331691  0.98559359  0.98361311  0.98583894\n",
      "   1.          0.98647977  0.95063167  0.95669644  0.85637498  0.9301213\n",
      "   0.92529877  0.92707621]\n",
      " [ 0.94001017  0.94875742  0.9614698   0.95640621  0.96591407  0.97228623\n",
      "   0.96450307  0.97294418  0.9749868   0.98104028  0.97293417  0.97847942\n",
      "   0.98647977  1.          0.95861707  0.96083563  0.86780617  0.93230361\n",
      "   0.93860531  0.93705079]\n",
      " [ 0.93913794  0.94562382  0.94981671  0.94198429  0.95085356  0.94847856\n",
      "   0.95153218  0.9553773   0.95079685  0.93017981  0.92654803  0.95362192\n",
      "   0.95063167  0.95861707  1.          0.98811627  0.92410614  0.93983382\n",
      "   0.94600397  0.92890432]\n",
      " [ 0.93517321  0.94030616  0.9483138   0.94724637  0.95424661  0.95155666\n",
      "   0.94782675  0.95818137  0.95533995  0.94091054  0.9382738   0.95686933\n",
      "   0.95669644  0.96083563  0.98811627  1.          0.93432176  0.95377746\n",
      "   0.95663009  0.93497572]\n",
      " [ 0.88926275  0.88976774  0.88408324  0.88101963  0.88451153  0.87544437\n",
      "   0.87670984  0.89352366  0.8712513   0.84065769  0.83602934  0.87253739\n",
      "   0.85637498  0.86780617  0.92410614  0.93432176  1.          0.88293029\n",
      "   0.88306982  0.83178186]\n",
      " [ 0.87371477  0.88260779  0.89507369  0.89631462  0.90744003  0.91687195\n",
      "   0.90770428  0.92205235  0.9118584   0.90904718  0.90422603  0.91899913\n",
      "   0.9301213   0.93230361  0.93983382  0.95377746  0.88293029  1.\n",
      "   0.97811773  0.9551168 ]\n",
      " [ 0.8746148   0.88757574  0.90054569  0.89401819  0.90828728  0.92061093\n",
      "   0.91087298  0.92280943  0.91297763  0.90800375  0.90173567  0.91504075\n",
      "   0.92529877  0.93860531  0.94600397  0.95663009  0.88306982  0.97811773\n",
      "   1.          0.97880923]\n",
      " [ 0.85069902  0.86376039  0.88345752  0.87617553  0.8937297   0.9105225\n",
      "   0.89665356  0.90724014  0.90858828  0.91538566  0.91171558  0.91435642\n",
      "   0.92707621  0.93705079  0.92890432  0.93497572  0.83178186  0.9551168\n",
      "   0.97880923  1.        ]]\n",
      "[[ 0.99383396  0.98209629  0.96497224  0.92598086  0.83598588  0.85821516\n",
      "   0.76104268  0.70664875  0.61878989  0.58393961  0.51269879  0.5172424\n",
      "   0.68572665  0.67944855  0.70581855  0.65098772  0.69521815  0.68834978\n",
      "   0.73313255  0.80590727]\n",
      " [ 1.          0.98716535  0.96690896  0.93207538  0.85054513  0.86931501\n",
      "   0.77438913  0.72673018  0.64128537  0.60763186  0.53908803  0.54549087\n",
      "   0.70562163  0.69535301  0.72062592  0.67390346  0.71285204  0.70393417\n",
      "   0.74443541  0.81308865]\n",
      " [ 0.98716535  1.          0.98754717  0.95122135  0.8777385   0.8939731\n",
      "   0.80000621  0.7578292   0.66908069  0.63729667  0.57048531  0.56936802\n",
      "   0.72993716  0.70954508  0.74898945  0.70158693  0.7449642   0.72621371\n",
      "   0.77146724  0.8303369 ]\n",
      " [ 0.96690896  0.98754717  1.          0.96109377  0.88450471  0.89743924\n",
      "   0.80501164  0.77253238  0.69558231  0.6668682   0.58960061  0.57044097\n",
      "   0.73286484  0.71413576  0.75866355  0.71374854  0.75133225  0.72485091\n",
      "   0.77341951  0.8298846 ]\n",
      " [ 0.93207538  0.95122135  0.96109377  1.          0.95413844  0.95118634\n",
      "   0.89608915  0.86956572  0.81526051  0.79078558  0.73023302  0.69717586\n",
      "   0.82634269  0.80484236  0.81570882  0.79794278  0.82186558  0.78106383\n",
      "   0.80985222  0.85305274]\n",
      " [ 0.85054513  0.8777385   0.88450471  0.95413844  1.          0.96703779\n",
      "   0.94036316  0.92340022  0.89051352  0.87736633  0.82675222  0.82097957\n",
      "   0.87705196  0.84763714  0.86678111  0.86261946  0.85540816  0.83068996\n",
      "   0.84691503  0.85498575]\n",
      " [ 0.86931501  0.8939731   0.89743924  0.95118634  0.96703779  1.\n",
      "   0.9250006   0.89949928  0.8412892   0.82057386  0.76563845  0.75898991\n",
      "   0.83984159  0.82083037  0.82888864  0.81867149  0.82206067  0.80897696\n",
      "   0.83352537  0.85514241]\n",
      " [ 0.77438913  0.80000621  0.80501164  0.89608915  0.94036316  0.9250006\n",
      "   1.          0.96147444  0.89325016  0.87807568  0.83347868  0.82330004\n",
      "   0.88072497  0.86557872  0.83649993  0.83985462  0.83516015  0.80068278\n",
      "   0.80822858  0.81671912]\n",
      " [ 0.72673018  0.7578292   0.77253238  0.86956572  0.92340022  0.89949928\n",
      "   0.96147444  1.          0.94099691  0.92073232  0.8813262   0.87264055\n",
      "   0.8943081   0.88309741  0.86906818  0.88381764  0.88341016  0.84457468\n",
      "   0.83893799  0.82155862]\n",
      " [ 0.64128537  0.66908069  0.69558231  0.81526051  0.89051352  0.8412892\n",
      "   0.89325016  0.94099691  1.          0.98813296  0.94409716  0.91485913\n",
      "   0.90192558  0.88712409  0.86448906  0.89394162  0.86153847  0.83314004\n",
      "   0.81912065  0.79091516]\n",
      " [ 0.60763186  0.63729667  0.6668682   0.79078558  0.87736633  0.82057386\n",
      "   0.87807568  0.92073232  0.98813296  1.          0.95726248  0.92658132\n",
      "   0.89979316  0.87898189  0.86849941  0.89847493  0.84697161  0.82587494\n",
      "   0.81009584  0.77741713]\n",
      " [ 0.53908803  0.57048531  0.58960061  0.73023302  0.82675222  0.76563845\n",
      "   0.83347868  0.8813262   0.94409716  0.95726248  1.          0.96593409\n",
      "   0.90774338  0.87628168  0.85693816  0.89396442  0.83028649  0.81312511\n",
      "   0.78053746  0.72274999]\n",
      " [ 0.54549087  0.56936802  0.57044097  0.69717586  0.82097957  0.75898991\n",
      "   0.82330004  0.87264055  0.91485913  0.92658132  0.96593409  1.\n",
      "   0.91753956  0.88691361  0.86652552  0.90376787  0.82500819  0.83213062\n",
      "   0.79273544  0.72736454]\n",
      " [ 0.70562163  0.72993716  0.73286484  0.82634269  0.87705196  0.83984159\n",
      "   0.88072497  0.8943081   0.90192558  0.89979316  0.90774338  0.91753956\n",
      "   1.          0.96985138  0.85127682  0.87617858  0.83924378  0.8128438\n",
      "   0.80602441  0.80231701]\n",
      " [ 0.69535301  0.70954508  0.71413576  0.80484236  0.84763714  0.82083037\n",
      "   0.86557872  0.88309741  0.88712409  0.87898189  0.87628168  0.88691361\n",
      "   0.96985138  1.          0.84967923  0.87577228  0.82662132  0.80637216\n",
      "   0.79941088  0.81355532]\n",
      " [ 0.72062592  0.74898945  0.75866355  0.81570882  0.86678111  0.82888864\n",
      "   0.83649993  0.86906818  0.86448906  0.86849941  0.85693816  0.86652552\n",
      "   0.85127682  0.84967923  1.          0.96406515  0.91322225  0.91193013\n",
      "   0.90015213  0.86659572]\n",
      " [ 0.67390346  0.70158693  0.71374854  0.79794278  0.86261946  0.81867149\n",
      "   0.83985462  0.88381764  0.89394162  0.89847493  0.89396442  0.90376787\n",
      "   0.87617858  0.87577228  0.96406515  1.          0.93091484  0.90708056\n",
      "   0.88626776  0.85547276]\n",
      " [ 0.71285204  0.7449642   0.75133225  0.82186558  0.85540816  0.82206067\n",
      "   0.83516015  0.88341016  0.86153847  0.84697161  0.83028649  0.82500819\n",
      "   0.83924378  0.82662132  0.91322225  0.93091484  1.          0.92027805\n",
      "   0.90299904  0.86641285]\n",
      " [ 0.70393417  0.72621371  0.72485091  0.78106383  0.83068996  0.80897696\n",
      "   0.80068278  0.84457468  0.83314004  0.82587494  0.81312511  0.83213062\n",
      "   0.8128438   0.80637216  0.91193013  0.90708056  0.92027805  1.\n",
      "   0.98364567  0.90569723]\n",
      " [ 0.74443541  0.77146724  0.77341951  0.80985222  0.84691503  0.83352537\n",
      "   0.80822858  0.83893799  0.81912065  0.81009584  0.78053746  0.79273544\n",
      "   0.80602441  0.79941088  0.90015213  0.88626776  0.90299904  0.98364567\n",
      "   1.          0.93146993]\n",
      " [ 0.81308865  0.8303369   0.8298846   0.85305274  0.85498575  0.85514241\n",
      "   0.81671912  0.82155862  0.79091516  0.77741713  0.72274999  0.72736454\n",
      "   0.80231701  0.81355532  0.86659572  0.85547276  0.86641285  0.90569723\n",
      "   0.93146993  1.        ]]\n",
      "[[ 0.99316948  0.98855001  0.98175133  0.9774663   0.97628461  0.96399014\n",
      "   0.96644857  0.96111692  0.96690474  0.96661151  0.96656469  0.9761022\n",
      "   0.97622303  0.96058064  0.92953551  0.90579706  0.92711696  0.91734362\n",
      "   0.92158282  0.92009627]\n",
      " [ 1.          0.99195522  0.97976755  0.97415407  0.96903873  0.95596867\n",
      "   0.95958442  0.95209654  0.96286282  0.96431925  0.96795031  0.97558909\n",
      "   0.97703534  0.95249404  0.93785642  0.92199882  0.92130984  0.92089529\n",
      "   0.91696138  0.9242894 ]\n",
      " [ 0.99195522  1.          0.99317113  0.98919276  0.98584945  0.97765222\n",
      "   0.97976157  0.97364233  0.97937787  0.9789553   0.9763265   0.98339994\n",
      "   0.97584631  0.96091959  0.94754981  0.92418631  0.93732336  0.92643973\n",
      "   0.93674681  0.9317203 ]\n",
      " [ 0.97976755  0.99317113  1.          0.99791482  0.99483496  0.98497668\n",
      "   0.98988776  0.98697205  0.98869211  0.98864458  0.97850184  0.97958652\n",
      "   0.96838467  0.9633791   0.95016971  0.92794695  0.9475425   0.93567222\n",
      "   0.94996744  0.94365832]\n",
      " [ 0.97415407  0.98919276  0.99791482  1.          0.99608125  0.98659399\n",
      "   0.99224245  0.9903346   0.99115353  0.99127687  0.97994288  0.9801214\n",
      "   0.96663196  0.96639465  0.94920708  0.92763859  0.94828259  0.93637382\n",
      "   0.95271858  0.94477505]\n",
      " [ 0.96903873  0.98584945  0.99483496  0.99608125  1.          0.99276379\n",
      "   0.99631359  0.99419527  0.99294739  0.99162725  0.98197632  0.98400396\n",
      "   0.97149043  0.97439743  0.94590771  0.91989293  0.95021389  0.92830191\n",
      "   0.9585801   0.94406147]\n",
      " [ 0.95596867  0.97765222  0.98497668  0.98659399  0.99276379  1.\n",
      "   0.99464549  0.99221089  0.98785156  0.98326508  0.97065451  0.97578834\n",
      "   0.958148    0.9689192   0.94210968  0.91375688  0.95268921  0.91902395\n",
      "   0.96192428  0.94130434]\n",
      " [ 0.95958442  0.97976157  0.98988776  0.99224245  0.99631359  0.99464549\n",
      "   1.          0.99609835  0.99464905  0.99191419  0.9811423   0.9794175\n",
      "   0.96583106  0.97328582  0.94301402  0.91617719  0.94986288  0.92123835\n",
      "   0.96030316  0.94282889]\n",
      " [ 0.95209654  0.97364233  0.98697205  0.9903346   0.99419527  0.99221089\n",
      "   0.99609835  1.          0.99428912  0.99110619  0.97520057  0.97564591\n",
      "   0.95801563  0.96727608  0.94379543  0.91777293  0.95765385  0.92086789\n",
      "   0.96508669  0.94773863]\n",
      " [ 0.96286282  0.97937787  0.98869211  0.99115353  0.99294739  0.98785156\n",
      "   0.99464905  0.99428912  1.          0.99763995  0.98921088  0.98676602\n",
      "   0.97368019  0.97072498  0.94388549  0.92398319  0.9453733   0.92288107\n",
      "   0.96671564  0.95442427]\n",
      " [ 0.96431925  0.9789553   0.98864458  0.99127687  0.99162725  0.98326508\n",
      "   0.99191419  0.99110619  0.99763995  1.          0.99163083  0.98739787\n",
      "   0.97503668  0.97042456  0.941271    0.92339025  0.94047239  0.9256661\n",
      "   0.96174171  0.95086814]\n",
      " [ 0.96795031  0.9763265   0.97850184  0.97994288  0.98197632  0.97065451\n",
      "   0.9811423   0.97520057  0.98921088  0.99163083  1.          0.9920696\n",
      "   0.98840213  0.9701949   0.92326876  0.90739809  0.91239589  0.90643145\n",
      "   0.94638043  0.93481057]\n",
      " [ 0.97558909  0.98339994  0.97958652  0.9801214   0.98400396  0.97578834\n",
      "   0.9794175   0.97564591  0.98676602  0.98739787  0.9920696   1.\n",
      "   0.9901894   0.97126917  0.93284728  0.91195129  0.92321096  0.91031117\n",
      "   0.9496631   0.93753956]\n",
      " [ 0.97703534  0.97584631  0.96838467  0.96663196  0.97149043  0.958148\n",
      "   0.96583106  0.95801563  0.97368019  0.97503668  0.98840213  0.9901894\n",
      "   1.          0.97392737  0.91760056  0.89634849  0.90278582  0.90063479\n",
      "   0.92882436  0.92038532]\n",
      " [ 0.95249404  0.96091959  0.9633791   0.96639465  0.97439743  0.9689192\n",
      "   0.97328582  0.96727608  0.97072498  0.97042456  0.9701949   0.97126917\n",
      "   0.97392737  1.          0.94180867  0.91616523  0.94159521  0.93782318\n",
      "   0.94789143  0.93535   ]\n",
      " [ 0.93785642  0.94754981  0.95016971  0.94920708  0.94590771  0.94210968\n",
      "   0.94301402  0.94379543  0.94388549  0.941271    0.92326876  0.93284728\n",
      "   0.91760056  0.94180867  1.          0.98926559  0.97729738  0.96635746\n",
      "   0.95023276  0.96032128]\n",
      " [ 0.92199882  0.92418631  0.92794695  0.92763859  0.91989293  0.91375688\n",
      "   0.91617719  0.91777293  0.92398319  0.92339025  0.90739809  0.91195129\n",
      "   0.89634849  0.91616523  0.98926559  1.          0.96102118  0.96077551\n",
      "   0.94057274  0.96349285]\n",
      " [ 0.92130984  0.93732336  0.9475425   0.94828259  0.95021389  0.95268921\n",
      "   0.94986288  0.95765385  0.9453733   0.94047239  0.91239589  0.92321096\n",
      "   0.90278582  0.94159521  0.97729738  0.96102118  1.          0.9576383\n",
      "   0.96619842  0.96296466]\n",
      " [ 0.92089529  0.92643973  0.93567222  0.93637382  0.92830191  0.91902395\n",
      "   0.92123835  0.92086789  0.92288107  0.9256661   0.90643145  0.91031117\n",
      "   0.90063479  0.93782318  0.96635746  0.96077551  0.9576383   1.\n",
      "   0.93742785  0.95279037]\n",
      " [ 0.91696138  0.93674681  0.94996744  0.95271858  0.9585801   0.96192428\n",
      "   0.96030316  0.96508669  0.96671564  0.96174171  0.94638043  0.9496631\n",
      "   0.92882436  0.94789143  0.95023276  0.94057274  0.96619842  0.93742785\n",
      "   1.          0.98430114]\n",
      " [ 0.9242894   0.9317203   0.94365832  0.94477505  0.94406147  0.94130434\n",
      "   0.94282889  0.94773863  0.95442427  0.95086814  0.93481057  0.93753956\n",
      "   0.92038532  0.93535     0.96032128  0.96349285  0.96296466  0.95279037\n",
      "   0.98430114  1.        ]]\n",
      "[[ 0.99965696  0.99424453  0.98682645  0.98411289  0.97223665  0.97426738\n",
      "   0.97777635  0.9576402   0.95357114  0.93541299  0.93913396  0.94412462\n",
      "   0.7004156   0.77545504  0.93725289  0.93622041  0.94679405  0.95900188\n",
      "   0.95763418  0.95744023]\n",
      " [ 1.          0.99479489  0.98604675  0.98399181  0.97107107  0.97483876\n",
      "   0.97734167  0.95699266  0.95282051  0.93380484  0.93824211  0.94559008\n",
      "   0.70256687  0.78021801  0.94116769  0.94108167  0.95263164  0.96050622\n",
      "   0.9601632   0.96213027]\n",
      " [ 0.99479489  1.          0.99533723  0.99341597  0.9805271   0.98448363\n",
      "   0.98696703  0.96821664  0.9670155   0.95022904  0.95148692  0.95888757\n",
      "   0.70873227  0.7843197   0.95332089  0.95006381  0.95891104  0.97269111\n",
      "   0.96889508  0.96511245]\n",
      " [ 0.98604675  0.99533723  1.          0.99849348  0.99246232  0.9921309\n",
      "   0.99475827  0.9838294   0.98381242  0.9728764   0.96824926  0.97160435\n",
      "   0.7054336   0.77435884  0.96133624  0.95385156  0.95312629  0.98187351\n",
      "   0.97423858  0.96024338]\n",
      " [ 0.98399181  0.99341597  0.99849348  1.          0.99457789  0.9954082\n",
      "   0.99673114  0.98796397  0.98663732  0.97585475  0.96892538  0.97648714\n",
      "   0.70667041  0.77837526  0.96837707  0.96118072  0.95863125  0.98555602\n",
      "   0.97792093  0.96547739]\n",
      " [ 0.97107107  0.9805271   0.99246232  0.99457789  1.          0.9972022\n",
      "   0.99783731  0.99628834  0.99488212  0.98842346  0.97696942  0.98184512\n",
      "   0.70008802  0.76674461  0.96994573  0.96063891  0.94877102  0.98805039\n",
      "   0.97739302  0.95876215]\n",
      " [ 0.97483876  0.98448363  0.9921309   0.9954082   0.9972022   1.\n",
      "   0.9977882   0.9950126   0.99336028  0.98437684  0.97680076  0.9864336\n",
      "   0.70733105  0.78075469  0.98077705  0.97436706  0.96598871  0.99316209\n",
      "   0.98564792  0.97282466]\n",
      " [ 0.97734167  0.98696703  0.99475827  0.99673114  0.99783731  0.9977882\n",
      "   1.          0.99227469  0.99059193  0.98153293  0.97229821  0.97892986\n",
      "   0.70469319  0.77480768  0.96951671  0.96095899  0.95642914  0.98683703\n",
      "   0.97878999  0.96368594]\n",
      " [ 0.95699266  0.96821664  0.9838294   0.98796397  0.99628834  0.9950126\n",
      "   0.99227469  1.          0.99637717  0.99309343  0.97799226  0.98662233\n",
      "   0.69362287  0.761576    0.97750236  0.96797877  0.94823306  0.98918461\n",
      "   0.978044    0.9578303 ]\n",
      " [ 0.95282051  0.9670155   0.98381242  0.98663732  0.99488212  0.99336028\n",
      "   0.99059193  0.99637717  1.          0.99780553  0.99014643  0.99130041\n",
      "   0.70553108  0.76964607  0.98084128  0.97135021  0.95430338  0.99394075\n",
      "   0.98495924  0.96072667]\n",
      " [ 0.93380484  0.95022904  0.9728764   0.97585475  0.98842346  0.98437684\n",
      "   0.98153293  0.99309343  0.99780553  1.          0.98938361  0.98769602\n",
      "   0.70118443  0.75899633  0.97403617  0.96212025  0.93895367  0.98699552\n",
      "   0.97664076  0.94557082]\n",
      " [ 0.93824211  0.95148692  0.96824926  0.96892538  0.97696942  0.97680076\n",
      "   0.97229821  0.97799226  0.99014643  0.98938361  1.          0.98892856\n",
      "   0.72103953  0.77939646  0.97511692  0.96862618  0.95459954  0.98821214\n",
      "   0.9852277   0.95802311]\n",
      " [ 0.94559008  0.95888757  0.97160435  0.97648714  0.98184512  0.9864336\n",
      "   0.97892986  0.98662233  0.99130041  0.98769602  0.98892856  1.\n",
      "   0.76251973  0.82660066  0.9888544   0.98375018  0.96824726  0.99288281\n",
      "   0.98946341  0.97146426]\n",
      " [ 0.70256687  0.70873227  0.7054336   0.70667041  0.70008802  0.70733105\n",
      "   0.70469319  0.69362287  0.70553108  0.70118443  0.72103953  0.76251973\n",
      "   1.          0.98582194  0.700888    0.70150036  0.70801993  0.70913519\n",
      "   0.71978915  0.70883279]\n",
      " [ 0.78021801  0.7843197   0.77435884  0.77837526  0.76674461  0.78075469\n",
      "   0.77480768  0.761576    0.76964607  0.75899633  0.77939646  0.82660066\n",
      "   0.98582194  1.          0.78355517  0.7882879   0.79973663  0.78404251\n",
      "   0.79694867  0.79830288]\n",
      " [ 0.94116769  0.95332089  0.96133624  0.96837707  0.96994573  0.98077705\n",
      "   0.96951671  0.97750236  0.98084128  0.97403617  0.97511692  0.9888544\n",
      "   0.700888    0.78355517  1.          0.99785313  0.98605894  0.99172153\n",
      "   0.99351558  0.98583669]\n",
      " [ 0.94108167  0.95006381  0.95385156  0.96118072  0.96063891  0.97436706\n",
      "   0.96095899  0.96797877  0.97135021  0.96212025  0.96862618  0.98375018\n",
      "   0.70150036  0.7882879   0.99785313  1.          0.99088153  0.98778985\n",
      "   0.99174497  0.9880897 ]\n",
      " [ 0.95263164  0.95891104  0.95312629  0.95863125  0.94877102  0.96598871\n",
      "   0.95642914  0.94823306  0.95430338  0.93895367  0.95459954  0.96824726\n",
      "   0.70801993  0.79973663  0.98605894  0.99088153  1.          0.9778884\n",
      "   0.98873632  0.99288971]\n",
      " [ 0.96050622  0.97269111  0.98187351  0.98555602  0.98805039  0.99316209\n",
      "   0.98683703  0.98918461  0.99394075  0.98699552  0.98821214  0.99288281\n",
      "   0.70913519  0.78404251  0.99172153  0.98778985  0.9778884   1.\n",
      "   0.9956457   0.98135972]\n",
      " [ 0.9601632   0.96889508  0.97423858  0.97792093  0.97739302  0.98564792\n",
      "   0.97878999  0.978044    0.98495924  0.97664076  0.9852277   0.98946341\n",
      "   0.71978915  0.79694867  0.99351558  0.99174497  0.98873632  0.9956457\n",
      "   1.          0.98994279]\n",
      " [ 0.96213027  0.96511245  0.96024338  0.96547739  0.95876215  0.97282466\n",
      "   0.96368594  0.9578303   0.96072667  0.94557082  0.95802311  0.97146426\n",
      "   0.70883279  0.79830288  0.98583669  0.9880897   0.99288971  0.98135972\n",
      "   0.98994279  1.        ]]\n",
      "[[ 0.97767824  0.95505814  0.94601938  0.94619676  0.93120056  0.89007131\n",
      "   0.88646443  0.86888546  0.78332606  0.72006756  0.6322601   0.54025897\n",
      "   0.54700876  0.18309174  0.24343236]\n",
      " [ 1.          0.97914376  0.97147701  0.9674936   0.95502613  0.90184743\n",
      "   0.89227783  0.87137676  0.79298119  0.7273274   0.64437895  0.54375865\n",
      "   0.54989844  0.14250119  0.26367857]\n",
      " [ 0.97914376  1.          0.99146807  0.98544795  0.97729971  0.92742817\n",
      "   0.91453259  0.89264791  0.82277301  0.75239474  0.6635188   0.55882943\n",
      "   0.56507185  0.12794015  0.22185579]\n",
      " [ 0.97147701  0.99146807  1.          0.98574658  0.97672897  0.92604207\n",
      "   0.91386023  0.89590785  0.83881949  0.77222899  0.68773519  0.58049295\n",
      "   0.58839998  0.13780038  0.23375401]\n",
      " [ 0.9674936   0.98544795  0.98574658  1.          0.98377974  0.91846613\n",
      "   0.91438113  0.89264732  0.802995    0.73558053  0.63926538  0.54701039\n",
      "   0.56365875  0.12234132  0.21083746]\n",
      " [ 0.95502613  0.97729971  0.97672897  0.98377974  1.          0.94214723\n",
      "   0.92783988  0.90107824  0.83945222  0.78567719  0.69049279  0.59900813\n",
      "   0.61900992  0.11867528  0.23280455]\n",
      " [ 0.90184743  0.92742817  0.92604207  0.91846613  0.94214723  1.\n",
      "   0.98668248  0.95659995  0.89075243  0.787382    0.70628003  0.63321885\n",
      "   0.6695634   0.1014846   0.23336024]\n",
      " [ 0.89227783  0.91453259  0.91386023  0.91438113  0.92783988  0.98668248\n",
      "   1.          0.97474142  0.89483682  0.78264282  0.69372296  0.6368017\n",
      "   0.67929016  0.09351163  0.2292707 ]\n",
      " [ 0.87137676  0.89264791  0.89590785  0.89264732  0.90107824  0.95659995\n",
      "   0.97474142  1.          0.9119444   0.79815817  0.7041641   0.65012086\n",
      "   0.68554638  0.11264633  0.22960318]\n",
      " [ 0.79298119  0.82277301  0.83881949  0.802995    0.83945222  0.89075243\n",
      "   0.89483682  0.9119444   1.          0.93417459  0.85986051  0.79951761\n",
      "   0.8049709   0.109687    0.32182038]\n",
      " [ 0.7273274   0.75239474  0.77222899  0.73558053  0.78567719  0.787382\n",
      "   0.78264282  0.79815817  0.93417459  1.          0.92236504  0.86537935\n",
      "   0.84623164  0.10926809  0.36724868]\n",
      " [ 0.64437895  0.6635188   0.68773519  0.63926538  0.69049279  0.70628003\n",
      "   0.69372296  0.7041641   0.85986051  0.92236504  1.          0.9415702\n",
      "   0.90964416  0.11624343  0.39665717]\n",
      " [ 0.54375865  0.55882943  0.58049295  0.54701039  0.59900813  0.63321885\n",
      "   0.6368017   0.65012086  0.79951761  0.86537935  0.9415702   1.\n",
      "   0.96207178  0.10706341  0.43604939]\n",
      " [ 0.54989844  0.56507185  0.58839998  0.56365875  0.61900992  0.6695634\n",
      "   0.67929016  0.68554638  0.8049709   0.84623164  0.90964416  0.96207178\n",
      "   1.          0.09403116  0.4174579 ]\n",
      " [ 0.14250119  0.12794015  0.13780038  0.12234132  0.11867528  0.1014846\n",
      "   0.09351163  0.11264633  0.109687    0.10926809  0.11624343  0.10706341\n",
      "   0.09403116  1.          0.30766113]\n",
      " [ 0.26367857  0.22185579  0.23375401  0.21083746  0.23280455  0.23336024\n",
      "   0.2292707   0.22960318  0.32182038  0.36724868  0.39665717  0.43604939\n",
      "   0.4174579   0.30766113  1.        ]]\n",
      "[[ 0.99462094  0.98475625  0.97704496  0.97768002  0.98333376  0.97041084\n",
      "   0.97380865  0.97481221  0.96865905  0.97055293  0.9595863   0.96496063\n",
      "   0.96197447  0.26600984  0.27363331]\n",
      " [ 1.          0.98854092  0.97983241  0.98244571  0.98411336  0.96700708\n",
      "   0.96684621  0.97041216  0.96253419  0.96609535  0.95893795  0.96375766\n",
      "   0.95901108  0.24455569  0.25156432]\n",
      " [ 0.98854092  1.          0.9950018   0.99286333  0.99103609  0.9771326\n",
      "   0.97615447  0.98068086  0.97919076  0.97912318  0.96559337  0.96566495\n",
      "   0.96111364  0.22676628  0.23326509]\n",
      " [ 0.97983241  0.9950018   1.          0.99492644  0.98601167  0.9672336\n",
      "   0.97063498  0.97811356  0.9785814   0.97663851  0.96596352  0.96407726\n",
      "   0.96011229  0.22433562  0.23076477]\n",
      " [ 0.98244571  0.99286333  0.99492644  1.          0.99117698  0.96743094\n",
      "   0.97075511  0.97679316  0.97460638  0.97540821  0.96872492  0.9700045\n",
      "   0.96597993  0.22356231  0.22996929]\n",
      " [ 0.98411336  0.99103609  0.98601167  0.99117698  1.          0.98776879\n",
      "   0.98682511  0.98631247  0.98184908  0.98096646  0.96943853  0.97000813\n",
      "   0.96494249  0.21893925  0.22521374]\n",
      " [ 0.96700708  0.9771326   0.9672336   0.96743094  0.98776879  1.\n",
      "   0.99159627  0.98532593  0.97870642  0.97325407  0.95065597  0.94640325\n",
      "   0.94108071  0.19081792  0.1962865 ]\n",
      " [ 0.96684621  0.97615447  0.97063498  0.97075511  0.98682511  0.99159627\n",
      "   1.          0.99123583  0.98756136  0.98229502  0.95813744  0.96044679\n",
      "   0.95665635  0.19758419  0.20324667]\n",
      " [ 0.97041216  0.98068086  0.97811356  0.97679316  0.98631247  0.98532593\n",
      "   0.99123583  1.          0.99040769  0.98766834  0.97196905  0.97301274\n",
      "   0.96366646  0.21518494  0.22135183]\n",
      " [ 0.96253419  0.97919076  0.9785814   0.97460638  0.98184908  0.97870642\n",
      "   0.98756136  0.99040769  1.          0.99583927  0.97479823  0.97429776\n",
      "   0.97257173  0.20524865  0.20721046]\n",
      " [ 0.96609535  0.97912318  0.97663851  0.97540821  0.98096646  0.97325407\n",
      "   0.98229502  0.98766834  0.99583927  1.          0.98545335  0.98276392\n",
      "   0.98054328  0.2153545   0.21705213]\n",
      " [ 0.95893795  0.96559337  0.96596352  0.96872492  0.96943853  0.95065597\n",
      "   0.95813744  0.97196905  0.97479823  0.98545335  1.          0.98649243\n",
      "   0.97639233  0.23610705  0.23780342]\n",
      " [ 0.96375766  0.96566495  0.96407726  0.9700045   0.97000813  0.94640325\n",
      "   0.96044679  0.97301274  0.97429776  0.98276392  0.98649243  1.\n",
      "   0.99126583  0.2474739   0.24443415]\n",
      " [ 0.95901108  0.96111364  0.96011229  0.96597993  0.96494249  0.94108071\n",
      "   0.95665635  0.96366646  0.97257173  0.98054328  0.97639233  0.99126583\n",
      "   1.          0.26781364  0.26068196]\n",
      " [ 0.24455569  0.22676628  0.22433562  0.22356231  0.21893925  0.19081792\n",
      "   0.19758419  0.21518494  0.20524865  0.2153545   0.23610705  0.2474739\n",
      "   0.26781364  1.          0.97213984]\n",
      " [ 0.25156432  0.23326509  0.23076477  0.22996929  0.22521374  0.1962865\n",
      "   0.20324667  0.22135183  0.20721046  0.21705213  0.23780342  0.24443415\n",
      "   0.26068196  0.97213984  1.        ]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "   0.98817177  0.96530423  0.94615374  0.93257142  0.9082218   0.82338193\n",
      "   0.7553452   0.8917898   0.7761488 ]\n",
      " [ 1.          0.99686373  0.99705019  0.99447277  0.99525069  0.98794592\n",
      "   0.99000813  0.97254563  0.95362577  0.93936547  0.91239599  0.83396398\n",
      "   0.77146884  0.89165295  0.78724722]\n",
      " [ 0.99686373  1.          0.99816131  0.9942327   0.99603176  0.99129837\n",
      "   0.99210887  0.97227382  0.95416884  0.94295714  0.91971059  0.83948869\n",
      "   0.77214049  0.90128758  0.79003086]\n",
      " [ 0.99705019  0.99816131  1.          0.99675671  0.99865467  0.9933298\n",
      "   0.9940017   0.97779649  0.95780239  0.94650105  0.9207568   0.84782588\n",
      "   0.7881411   0.89155936  0.78184672]\n",
      " [ 0.99447277  0.9942327   0.99675671  1.          0.99737189  0.99464107\n",
      "   0.99395393  0.98064415  0.9576079   0.94809673  0.92417131  0.85618521\n",
      "   0.79526601  0.88436626  0.7754132 ]\n",
      " [ 0.99525069  0.99603176  0.99865467  0.99737189  1.          0.99579557\n",
      "   0.99586425  0.98264712  0.961954    0.95069053  0.92366148  0.853696\n",
      "   0.79567441  0.88465354  0.77625696]\n",
      " [ 0.98794592  0.99129837  0.9933298   0.99464107  0.99579557  1.\n",
      "   0.9966315   0.98382254  0.9531838   0.94760691  0.92428098  0.8565596\n",
      "   0.79584104  0.87797261  0.76029635]\n",
      " [ 0.99000813  0.99210887  0.9940017   0.99395393  0.99586425  0.9966315\n",
      "   1.          0.9890267   0.96451789  0.95702749  0.92992127  0.86552749\n",
      "   0.80641706  0.88166559  0.77513424]\n",
      " [ 0.97254563  0.97227382  0.97779649  0.98064415  0.98264712  0.98382254\n",
      "   0.9890267   1.          0.97519502  0.97022708  0.93921769  0.89998901\n",
      "   0.85439063  0.850806    0.76373753]\n",
      " [ 0.95362577  0.95416884  0.95780239  0.9576079   0.961954    0.9531838\n",
      "   0.96451789  0.97519502  1.          0.99308797  0.96806909  0.93322892\n",
      "   0.88876499  0.87723366  0.80165278]\n",
      " [ 0.93936547  0.94295714  0.94650105  0.94809673  0.95069053  0.94760691\n",
      "   0.95702749  0.97022708  0.99308797  1.          0.9822957   0.95530787\n",
      "   0.91480363  0.88052853  0.8043066 ]\n",
      " [ 0.91239599  0.91971059  0.9207568   0.92417131  0.92366148  0.92428098\n",
      "   0.92992127  0.93921769  0.96806909  0.9822957   1.          0.97180775\n",
      "   0.92356055  0.91041774  0.82806112]\n",
      " [ 0.83396398  0.83948869  0.84782588  0.85618521  0.853696    0.8565596\n",
      "   0.86552749  0.89998901  0.93322892  0.95530787  0.97180775  1.\n",
      "   0.97286945  0.83678584  0.79019168]\n",
      " [ 0.77146884  0.77214049  0.7881411   0.79526601  0.79567441  0.79584104\n",
      "   0.80641706  0.85439063  0.88876499  0.91480363  0.92356055  0.97286945\n",
      "   1.          0.7676429   0.7314605 ]\n",
      " [ 0.89165295  0.90128758  0.89155936  0.88436626  0.88465354  0.87797261\n",
      "   0.88166559  0.850806    0.87723366  0.88052853  0.91041774  0.83678584\n",
      "   0.7676429   1.          0.93931134]\n",
      " [ 0.78724722  0.79003086  0.78184672  0.7754132   0.77625696  0.76029635\n",
      "   0.77513424  0.76373753  0.80165278  0.8043066   0.82806112  0.79019168\n",
      "   0.7314605   0.93931134  1.        ]]\n",
      "[[ 0.97059078  0.91978393  0.90184694  0.91289235  0.90615116  0.88521618\n",
      "   0.79182268  0.85742633  0.83932002  0.83412744  0.84205768  0.81752557\n",
      "   0.80190272  0.67357258  0.67761581]\n",
      " [ 1.          0.95015035  0.93968698  0.93773443  0.92791436  0.90987924\n",
      "   0.80518482  0.88429769  0.85918441  0.85559946  0.86566371  0.84325052\n",
      "   0.81454861  0.68145882  0.68451403]\n",
      " [ 0.95015035  1.          0.97814636  0.97991869  0.98709076  0.96654043\n",
      "   0.87012335  0.92660727  0.92263354  0.9219083   0.92733329  0.91239964\n",
      "   0.87781762  0.77681137  0.77782829]\n",
      " [ 0.93968698  0.97814636  1.          0.96314565  0.95865099  0.93327807\n",
      "   0.82523855  0.92176549  0.88682102  0.88035415  0.89948021  0.89287233\n",
      "   0.83273084  0.70646945  0.70712706]\n",
      " [ 0.93773443  0.97991869  0.96314565  1.          0.98378321  0.96500351\n",
      "   0.85384054  0.91183227  0.9055849   0.9067144   0.9064491   0.88384606\n",
      "   0.87488143  0.73690175  0.74031418]\n",
      " [ 0.92791436  0.98709076  0.95865099  0.98378321  1.          0.98026777\n",
      "   0.86618925  0.924914    0.92916255  0.93463231  0.93270091  0.91355506\n",
      "   0.89744852  0.79302211  0.79394331]\n",
      " [ 0.90987924  0.96654043  0.93327807  0.96500351  0.98026777  1.\n",
      "   0.8670866   0.90805575  0.9110166   0.92220892  0.92648699  0.8994816\n",
      "   0.8826535   0.77328421  0.77702217]\n",
      " [ 0.80518482  0.87012335  0.82523855  0.85384054  0.86618925  0.8670866\n",
      "   1.          0.84460662  0.84410971  0.85641234  0.85781587  0.8391749\n",
      "   0.83004273  0.73617433  0.7436474 ]\n",
      " [ 0.88429769  0.92660727  0.92176549  0.91183227  0.924914    0.90805575\n",
      "   0.84460662  1.          0.95765782  0.94770131  0.95337316  0.94756088\n",
      "   0.91757162  0.77895484  0.77973868]\n",
      " [ 0.85918441  0.92263354  0.88682102  0.9055849   0.92916255  0.9110166\n",
      "   0.84410971  0.95765782  1.          0.98587845  0.9768902   0.96304242\n",
      "   0.9530617   0.86975487  0.86982166]\n",
      " [ 0.85559946  0.9219083   0.88035415  0.9067144   0.93463231  0.92220892\n",
      "   0.85641234  0.94770131  0.98587845  1.          0.98535472  0.97095121\n",
      "   0.96020518  0.88334747  0.88317916]\n",
      " [ 0.86566371  0.92733329  0.89948021  0.9064491   0.93270091  0.92648699\n",
      "   0.85781587  0.95337316  0.9768902   0.98535472  1.          0.98593183\n",
      "   0.95352146  0.87860788  0.87786915]\n",
      " [ 0.84325052  0.91239964  0.89287233  0.88384606  0.91355506  0.8994816\n",
      "   0.8391749   0.94756088  0.96304242  0.97095121  0.98593183  1.\n",
      "   0.95794788  0.87737623  0.87466413]\n",
      " [ 0.81454861  0.87781762  0.83273084  0.87488143  0.89744852  0.8826535\n",
      "   0.83004273  0.91757162  0.9530617   0.96020518  0.95352146  0.95794788\n",
      "   1.          0.86654143  0.87004039]\n",
      " [ 0.68145882  0.77681137  0.70646945  0.73690175  0.79302211  0.77328421\n",
      "   0.73617433  0.77895484  0.86975487  0.88334747  0.87860788  0.87737623\n",
      "   0.86654143  1.          0.99684815]\n",
      " [ 0.68451403  0.77782829  0.70712706  0.74031418  0.79394331  0.77702217\n",
      "   0.7436474   0.77973868  0.86982166  0.88317916  0.87786915  0.87466413\n",
      "   0.87004039  0.99684815  1.        ]]\n",
      "[[ 0.99717663  0.99635973  0.99321621  0.99152004  0.9937705   0.98071104\n",
      "   0.96652486  0.96511668  0.93326053  0.8586477   0.79423223  0.8213288\n",
      "   0.89776372  0.5485538   0.55816771]\n",
      " [ 1.          0.99684345  0.99461256  0.98921439  0.99240508  0.98117132\n",
      "   0.96725789  0.96587766  0.93177951  0.85352562  0.78706212  0.81055615\n",
      "   0.894766    0.52989879  0.54910871]\n",
      " [ 0.99684345  1.          0.99768461  0.98628872  0.99068011  0.9747761\n",
      "   0.95867772  0.95810937  0.93595092  0.86589842  0.80392581  0.82427539\n",
      "   0.90229759  0.54314643  0.56059058]\n",
      " [ 0.99461256  0.99768461  1.          0.98145125  0.9866481   0.96736435\n",
      "   0.94893257  0.95118388  0.95091103  0.88714614  0.82747402  0.8465176\n",
      "   0.92123506  0.56764762  0.59107201]\n",
      " [ 0.98921439  0.98628872  0.98145125  1.          0.99815052  0.99068212\n",
      "   0.98072458  0.98251849  0.90944776  0.81688033  0.74120689  0.77699076\n",
      "   0.8624043   0.52325936  0.52803366]\n",
      " [ 0.99240508  0.99068011  0.9866481   0.99815052  1.          0.98845936\n",
      "   0.97633748  0.97640337  0.91660605  0.83077192  0.75873644  0.79042756\n",
      "   0.87496542  0.52577691  0.5334973 ]\n",
      " [ 0.98117132  0.9747761   0.96736435  0.99068212  0.98845936  1.\n",
      "   0.99629436  0.9920649   0.88177223  0.76862431  0.68483037  0.72352213\n",
      "   0.8280199   0.45129614  0.46108315]\n",
      " [ 0.96725789  0.95867772  0.94893257  0.98072458  0.97633748  0.99629436\n",
      "   1.          0.99312237  0.85452691  0.72809922  0.63831471  0.68155305\n",
      "   0.79503674  0.40521096  0.41322682]\n",
      " [ 0.96587766  0.95810937  0.95118388  0.98251849  0.97640337  0.9920649\n",
      "   0.99312237  1.          0.87738234  0.75238014  0.66020801  0.70275682\n",
      "   0.80820026  0.4531584   0.46357883]\n",
      " [ 0.93177951  0.93595092  0.95091103  0.90944776  0.91660605  0.88177223\n",
      "   0.85452691  0.87738234  1.          0.97083232  0.92485879  0.93667144\n",
      "   0.9661674   0.74654687  0.77642047]\n",
      " [ 0.85352562  0.86589842  0.88714614  0.81688033  0.83077192  0.76862431\n",
      "   0.72809922  0.75238014  0.97083232  1.          0.98584716  0.98411462\n",
      "   0.97042199  0.81794211  0.8450694 ]\n",
      " [ 0.78706212  0.80392581  0.82747402  0.74120689  0.75873644  0.68483037\n",
      "   0.63831471  0.66020801  0.92485879  0.98584716  1.          0.98825679\n",
      "   0.95172177  0.83089718  0.85674565]\n",
      " [ 0.81055615  0.82427539  0.8465176   0.77699076  0.79042756  0.72352213\n",
      "   0.68155305  0.70275682  0.93667144  0.98411462  0.98825679  1.\n",
      "   0.96987755  0.83621131  0.85546255]\n",
      " [ 0.894766    0.90229759  0.92123506  0.8624043   0.87496542  0.8280199\n",
      "   0.79503674  0.80820026  0.9661674   0.97042199  0.95172177  0.96987755\n",
      "   1.          0.72643466  0.76534358]\n",
      " [ 0.52989879  0.54314643  0.56764762  0.52325936  0.52577691  0.45129614\n",
      "   0.40521096  0.4531584   0.74654687  0.81794211  0.83089718  0.83621131\n",
      "   0.72643466  1.          0.98441737]\n",
      " [ 0.54910871  0.56059058  0.59107201  0.52803366  0.5334973   0.46108315\n",
      "   0.41322682  0.46357883  0.77642047  0.8450694   0.85674565  0.85546255\n",
      "   0.76534358  0.98441737  1.        ]]\n",
      "[[ 0.99967587  0.99948316  0.99800978  0.99162962  0.9961923   0.96516878\n",
      "   0.91466661  0.91111584  0.94234626  0.9684393   0.98914876  0.99447692\n",
      "   0.98579966  0.1652096   0.21033084]\n",
      " [ 1.          0.99902047  0.99736335  0.99065323  0.99574931  0.96204313\n",
      "   0.91107321  0.90700593  0.93964903  0.96451029  0.98657728  0.99298531\n",
      "   0.9846203   0.18471005  0.22633364]\n",
      " [ 0.99902047  1.          0.99719974  0.98935164  0.99431567  0.97261193\n",
      "   0.92692275  0.92333883  0.95244698  0.97429421  0.99167937  0.99497983\n",
      "   0.98648197  0.1803696   0.22411169]\n",
      " [ 0.99736335  0.99719974  1.          0.99742141  0.9993073   0.96256634\n",
      "   0.90954429  0.9027022   0.94062853  0.97254665  0.99235559  0.99809397\n",
      "   0.99349073  0.14359767  0.16494778]\n",
      " [ 0.99065323  0.98935164  0.99742141  1.          0.99887819  0.94680375\n",
      "   0.88621088  0.87706105  0.92259081  0.96493336  0.98752304  0.99565805\n",
      "   0.9940346   0.09896885  0.10296155]\n",
      " [ 0.99574931  0.99431567  0.9993073   0.99887819  1.          0.95300554\n",
      "   0.89482228  0.88681536  0.92842609  0.9652896   0.98822056  0.99627468\n",
      "   0.99268621  0.12835117  0.1451811 ]\n",
      " [ 0.96204313  0.97261193  0.96256634  0.94680375  0.95300554  1.\n",
      "   0.98713368  0.98246287  0.99354321  0.99144204  0.98290602  0.9707084\n",
      "   0.96444386  0.21508061  0.25746193]\n",
      " [ 0.91107321  0.92692275  0.90954429  0.88621088  0.89482228  0.98713368\n",
      "   1.          0.99601506  0.99395818  0.96936555  0.94510882  0.92371983\n",
      "   0.91738697  0.28438583  0.32518867]\n",
      " [ 0.90700593  0.92333883  0.9027022   0.87706105  0.88681536  0.98246287\n",
      "   0.99601506  1.          0.98883132  0.96243245  0.93893199  0.91587795\n",
      "   0.90427638  0.27046669  0.33746298]\n",
      " [ 0.93964903  0.95244698  0.94062853  0.92259081  0.92842609  0.99354321\n",
      "   0.99395818  0.98883132  1.          0.98662728  0.96991554  0.953158\n",
      "   0.94899385  0.27529867  0.29049126]\n",
      " [ 0.96451029  0.97429421  0.97254665  0.96493336  0.9652896   0.99144204\n",
      "   0.96936555  0.96243245  0.98662728  1.          0.99356119  0.98318979\n",
      "   0.98125721  0.1513524   0.1574584 ]\n",
      " [ 0.98657728  0.99167937  0.99235559  0.98752304  0.98822056  0.98290602\n",
      "   0.94510882  0.93893199  0.96991554  0.99356119  1.          0.99724209\n",
      "   0.99346515  0.1391521   0.15383082]\n",
      " [ 0.99298531  0.99497983  0.99809397  0.99565805  0.99627468  0.9707084\n",
      "   0.92371983  0.91587795  0.953158    0.98318979  0.99724209  1.\n",
      "   0.99670994  0.13717788  0.14842282]\n",
      " [ 0.9846203   0.98648197  0.99349073  0.9940346   0.99268621  0.96444386\n",
      "   0.91738697  0.90427638  0.94899385  0.98125721  0.99346515  0.99670994\n",
      "   1.          0.14454036  0.12388926]\n",
      " [ 0.18471005  0.1803696   0.14359767  0.09896885  0.12835117  0.21508061\n",
      "   0.28438583  0.27046669  0.27529867  0.1513524   0.1391521   0.13717788\n",
      "   0.14454036  1.          0.89238207]\n",
      " [ 0.22633364  0.22411169  0.16494778  0.10296155  0.1451811   0.25746193\n",
      "   0.32518867  0.33746298  0.29049126  0.1574584   0.15383082  0.14842282\n",
      "   0.12388926  0.89238207  1.        ]]\n",
      "[[ 0.79076698  0.73797751  0.7421737   0.72415657  0.69093561  0.61505266\n",
      "   0.6361694   0.58486206  0.53276648  0.65798737  0.6760713   0.44310157\n",
      "   0.38209517  0.37262982  0.29801101  0.45156117  0.3882838   0.46696523]\n",
      " [ 1.          0.98020326  0.97602009  0.9660308   0.92123986  0.81264114\n",
      "   0.82984687  0.83498423  0.76406266  0.87093245  0.90223198  0.66969976\n",
      "   0.58378396  0.54517332  0.33313518  0.52695137  0.43416674  0.54959266]\n",
      " [ 0.98020326  1.          0.98787251  0.98036112  0.93743007  0.82744809\n",
      "   0.85418048  0.88244924  0.80839895  0.88382345  0.91507782  0.71270443\n",
      "   0.62506979  0.56792451  0.30675322  0.48945691  0.38875924  0.50760306]\n",
      " [ 0.97602009  0.98787251  1.          0.98943455  0.95849768  0.85386154\n",
      "   0.87508366  0.8971016   0.82141168  0.88764275  0.91650736  0.71097631\n",
      "   0.6270816   0.5642395   0.30384786  0.50514936  0.37787534  0.52754721]\n",
      " [ 0.9660308   0.98036112  0.98943455  1.          0.96488425  0.8683872\n",
      "   0.88315667  0.90474045  0.83637549  0.88832307  0.91056449  0.72142594\n",
      "   0.63930203  0.57428657  0.30399243  0.49564356  0.38341604  0.51113737]\n",
      " [ 0.92123986  0.93743007  0.95849768  0.96488425  1.          0.94239432\n",
      "   0.92951972  0.90548899  0.83707308  0.85422512  0.8782728   0.70186265\n",
      "   0.62199239  0.55080658  0.29012334  0.47842516  0.34921064  0.50958649]\n",
      " [ 0.81264114  0.82744809  0.85386154  0.8683872   0.94239432  1.\n",
      "   0.96043746  0.85739496  0.78976801  0.78901402  0.78145252  0.63127026\n",
      "   0.56299478  0.4998692   0.26343383  0.41516927  0.31185863  0.4423626 ]\n",
      " [ 0.82984687  0.85418048  0.87508366  0.88315667  0.92951972  0.96043746\n",
      "   1.          0.90510437  0.83490044  0.82507532  0.81333315  0.65316867\n",
      "   0.57945386  0.5104126   0.27208138  0.43857273  0.32058038  0.44769051]\n",
      " [ 0.83498423  0.88244924  0.8971016   0.90474045  0.90548899  0.85739496\n",
      "   0.90510437  1.          0.95690675  0.89927299  0.88102163  0.74537279\n",
      "   0.67075963  0.580208    0.26753561  0.43139102  0.30609028  0.41145341]\n",
      " [ 0.76406266  0.80839895  0.82141168  0.83637549  0.83707308  0.78976801\n",
      "   0.83490044  0.95690675  1.          0.88037661  0.82617129  0.69406656\n",
      "   0.62455505  0.5380294   0.22562393  0.38493396  0.26867441  0.36718246]\n",
      " [ 0.87093245  0.88382345  0.88764275  0.88832307  0.85422512  0.78901402\n",
      "   0.82507532  0.89927299  0.88037661  1.          0.93582338  0.80107179\n",
      "   0.70957599  0.65434843  0.35699467  0.47534823  0.47606764  0.46737703]\n",
      " [ 0.90223198  0.91507782  0.91650736  0.91056449  0.8782728   0.78145252\n",
      "   0.81333315  0.88102163  0.82617129  0.93582338  1.          0.83995815\n",
      "   0.74158153  0.6770959   0.36556512  0.48647987  0.47853254  0.47360899]\n",
      " [ 0.66969976  0.71270443  0.71097631  0.72142594  0.70186265  0.63127026\n",
      "   0.65316867  0.74537279  0.69406656  0.80107179  0.83995815  1.\n",
      "   0.93338407  0.88868421  0.54584539  0.38580515  0.46730541  0.34263507]\n",
      " [ 0.58378396  0.62506979  0.6270816   0.63930203  0.62199239  0.56299478\n",
      "   0.57945386  0.67075963  0.62455505  0.70957599  0.74158153  0.93338407\n",
      "   1.          0.95430743  0.63011533  0.36762538  0.46612145  0.31564111]\n",
      " [ 0.54517332  0.56792451  0.5642395   0.57428657  0.55080658  0.4998692\n",
      "   0.5104126   0.580208    0.5380294   0.65434843  0.6770959   0.88868421\n",
      "   0.95430743  1.          0.68100247  0.36890924  0.48482731  0.31371307]\n",
      " [ 0.33313518  0.30675322  0.30384786  0.30399243  0.29012334  0.26343383\n",
      "   0.27208138  0.26753561  0.22562393  0.35699467  0.36556512  0.54584539\n",
      "   0.63011533  0.68100247  1.          0.42918313  0.51519522  0.3012635 ]\n",
      " [ 0.52695137  0.48945691  0.50514936  0.49564356  0.47842516  0.41516927\n",
      "   0.43857273  0.43139102  0.38493396  0.47534823  0.48647987  0.38580515\n",
      "   0.36762538  0.36890924  0.42918313  1.          0.61918491  0.60179097]\n",
      " [ 0.43416674  0.38875924  0.37787534  0.38341604  0.34921064  0.31185863\n",
      "   0.32058038  0.30609028  0.26867441  0.47606764  0.47853254  0.46730541\n",
      "   0.46612145  0.48482731  0.51519522  0.61918491  1.          0.47730392]\n",
      " [ 0.54959266  0.50760306  0.52754721  0.51113737  0.50958649  0.4423626\n",
      "   0.44769051  0.41145341  0.36718246  0.46737703  0.47360899  0.34263507\n",
      "   0.31564111  0.31371307  0.3012635   0.60179097  0.47730392  1.        ]]\n",
      "[[ 0.99012462  0.98435813  0.97509912  0.98502113  0.98617175  0.96005681\n",
      "   0.97754097  0.97976581  0.94628779  0.97125992  0.96444793  0.93622439\n",
      "   0.89966243  0.89490252  0.75045238  0.51496083  0.66347166  0.30835677]\n",
      " [ 1.          0.99538075  0.9812359   0.98494817  0.98346619  0.96651542\n",
      "   0.96288698  0.96489614  0.94271762  0.96398451  0.95928937  0.92668192\n",
      "   0.89032683  0.88324717  0.75598616  0.49047278  0.66617642  0.30367792]\n",
      " [ 0.99538075  1.          0.98265392  0.98180999  0.97948773  0.96537354\n",
      "   0.95707739  0.96420762  0.94923281  0.96330986  0.96038843  0.93078467\n",
      "   0.90152182  0.89440726  0.77336098  0.48511704  0.67564205  0.30423059]\n",
      " [ 0.9812359   0.98265392  1.          0.99438647  0.98845684  0.97540657\n",
      "   0.95439095  0.96407061  0.95727115  0.97150565  0.96759403  0.92846476\n",
      "   0.91505265  0.90027203  0.79729607  0.48933532  0.70812832  0.30785666]\n",
      " [ 0.98494817  0.98180999  0.99438647  1.          0.99349505  0.97450949\n",
      "   0.96653881  0.96659472  0.9485802   0.96768175  0.96317121  0.92323128\n",
      "   0.89940588  0.88955882  0.77125273  0.48983914  0.67922921  0.30415214]\n",
      " [ 0.98346619  0.97948773  0.98845684  0.99349505  1.          0.98139838\n",
      "   0.97517627  0.97604869  0.9595739   0.97177361  0.96684463  0.9290897\n",
      "   0.90719758  0.90110494  0.77587411  0.49012317  0.6764681   0.31910007]\n",
      " [ 0.96651542  0.96537354  0.97540657  0.97450949  0.98139838  1.\n",
      "   0.9728571   0.95293116  0.94498179  0.95678085  0.95154644  0.91364537\n",
      "   0.90857008  0.89195665  0.79184344  0.49664657  0.71579666  0.34818001]\n",
      " [ 0.96288698  0.95707739  0.95439095  0.96653881  0.97517627  0.9728571\n",
      "   1.          0.96880161  0.92962734  0.95655314  0.94677457  0.92468574\n",
      "   0.89546861  0.88499016  0.74244853  0.53874818  0.6761821   0.33344388]\n",
      " [ 0.96489614  0.96420762  0.96407061  0.96659472  0.97604869  0.95293116\n",
      "   0.96880161  1.          0.98099708  0.98409217  0.9795228   0.9649895\n",
      "   0.94438978  0.94778385  0.80976094  0.51035581  0.69032253  0.32389777]\n",
      " [ 0.94271762  0.94923281  0.95727115  0.9485802   0.9595739   0.94498179\n",
      "   0.92962734  0.98099708  1.          0.97689093  0.97903661  0.95268243\n",
      "   0.95786416  0.96134934  0.85217334  0.45885803  0.70340608  0.30965588]\n",
      " [ 0.96398451  0.96330986  0.97150565  0.96768175  0.97177361  0.95678085\n",
      "   0.95655314  0.98409217  0.97689093  1.          0.99309083  0.94667522\n",
      "   0.93406212  0.92471118  0.80377404  0.50900983  0.71145275  0.31385381]\n",
      " [ 0.95928937  0.96038843  0.96759403  0.96317121  0.96684463  0.95154644\n",
      "   0.94677457  0.9795228   0.97903661  0.99309083  1.          0.94811063\n",
      "   0.93389269  0.92761599  0.81200377  0.4871548   0.70066843  0.31004781]\n",
      " [ 0.92668192  0.93078467  0.92846476  0.92323128  0.9290897   0.91364537\n",
      "   0.92468574  0.9649895   0.95268243  0.94667522  0.94811063  1.\n",
      "   0.96413055  0.96164636  0.86105861  0.53864881  0.74104016  0.34761176]\n",
      " [ 0.89032683  0.90152182  0.91505265  0.89940588  0.90719758  0.90857008\n",
      "   0.89546861  0.94438978  0.95786416  0.93406212  0.93389269  0.96413055\n",
      "   1.          0.98547941  0.91696952  0.53260513  0.81044341  0.33699456]\n",
      " [ 0.88324717  0.89440726  0.90027203  0.88955882  0.90110494  0.89195665\n",
      "   0.88499016  0.94778385  0.96134934  0.92471118  0.92761599  0.96164636\n",
      "   0.98547941  1.          0.9138808   0.51163566  0.7703695   0.3445668 ]\n",
      " [ 0.75598616  0.77336098  0.79729607  0.77125273  0.77587411  0.79184344\n",
      "   0.74244853  0.80976094  0.85217334  0.80377404  0.81200377  0.86105861\n",
      "   0.91696952  0.9138808   1.          0.49672413  0.84090249  0.29504039]\n",
      " [ 0.49047278  0.48511704  0.48933532  0.48983914  0.49012317  0.49664657\n",
      "   0.53874818  0.51035581  0.45885803  0.50900983  0.4871548   0.53864881\n",
      "   0.53260513  0.51163566  0.49672413  1.          0.74155615  0.32493524]\n",
      " [ 0.66617642  0.67564205  0.70812832  0.67922921  0.6764681   0.71579666\n",
      "   0.6761821   0.69032253  0.70340608  0.71145275  0.70066843  0.74104016\n",
      "   0.81044341  0.7703695   0.84090249  0.74155615  1.          0.3416517 ]\n",
      " [ 0.30367792  0.30423059  0.30785666  0.30415214  0.31910007  0.34818001\n",
      "   0.33344388  0.32389777  0.30965588  0.31385381  0.31004781  0.34761176\n",
      "   0.33699456  0.3445668   0.29504039  0.32493524  0.3416517   1.        ]]\n",
      "[[ 0.9958526   0.99665927  0.99702022  0.99723228  0.99520519  0.99318776\n",
      "   0.99471134  0.99260559  0.99266711  0.9852423   0.9920992   0.88944262\n",
      "   0.75988406  0.8596411   0.8901915   0.8870908   0.74257865  0.90022574]\n",
      " [ 1.          0.99865677  0.99782289  0.99775155  0.99727616  0.99667475\n",
      "   0.99392965  0.99391542  0.99285005  0.99184847  0.99319235  0.9098589\n",
      "   0.79712131  0.8839502   0.9024741   0.90017665  0.76947228  0.90644409]\n",
      " [ 0.99865677  1.          0.99868588  0.998428    0.99709364  0.99711819\n",
      "   0.99510673  0.99309649  0.99320802  0.9903171   0.99372838  0.9019723\n",
      "   0.78475807  0.87698988  0.90228274  0.8995782   0.76226611  0.9087494 ]\n",
      " [ 0.99782289  0.99868588  1.          0.99971688  0.99813453  0.99732902\n",
      "   0.99617731  0.99337682  0.99398506  0.98974685  0.99312529  0.90052481\n",
      "   0.78079704  0.87546299  0.90483929  0.90389581  0.76199711  0.91417153]\n",
      " [ 0.99775155  0.998428    0.99971688  1.          0.99846676  0.99755834\n",
      "   0.99641144  0.99422407  0.99428438  0.99030114  0.99295708  0.90122197\n",
      "   0.78154657  0.87598655  0.90346169  0.90228656  0.76227065  0.9120898 ]\n",
      " [ 0.99727616  0.99709364  0.99813453  0.99846676  1.          0.99691236\n",
      "   0.99535946  0.99426352  0.99311942  0.99068463  0.99208438  0.90894767\n",
      "   0.79273953  0.8836597   0.90651708  0.90412669  0.76750741  0.91086129]\n",
      " [ 0.99667475  0.99711819  0.99732902  0.99755834  0.99691236  1.\n",
      "   0.9967035   0.9931026   0.99326216  0.99334831  0.99342518  0.91138696\n",
      "   0.8048946   0.89301333  0.9150118   0.91256583  0.78646305  0.91586182]\n",
      " [ 0.99392965  0.99510673  0.99617731  0.99641144  0.99535946  0.9967035\n",
      "   1.          0.99365323  0.9930524   0.98619299  0.99016674  0.89338071\n",
      "   0.77267106  0.86938135  0.90576106  0.90742296  0.76416323  0.91439593]\n",
      " [ 0.99391542  0.99309649  0.99337682  0.99422407  0.99426352  0.9931026\n",
      "   0.99365323  1.          0.99483398  0.99108002  0.99166157  0.91725929\n",
      "   0.79856912  0.8893305   0.90632368  0.89751955  0.77056553  0.89542918]\n",
      " [ 0.99285005  0.99320802  0.99398506  0.99428438  0.99311942  0.99326216\n",
      "   0.9930524   0.99483398  1.          0.99286461  0.9945096   0.91369133\n",
      "   0.79738849  0.88819994  0.91331439  0.90530461  0.78130621  0.90901264]\n",
      " [ 0.99184847  0.9903171   0.98974685  0.99030114  0.99068463  0.99334831\n",
      "   0.98619299  0.99108002  0.99286461  1.          0.99455693  0.9392554\n",
      "   0.84509779  0.91981806  0.9225725   0.9079813   0.81052132  0.90168197]\n",
      " [ 0.99319235  0.99372838  0.99312529  0.99295708  0.99208438  0.99342518\n",
      "   0.99016674  0.99166157  0.9945096   0.99455693  1.          0.91993745\n",
      "   0.80891829  0.89509928  0.91080202  0.89985734  0.78091393  0.90082747]\n",
      " [ 0.9098589   0.9019723   0.90052481  0.90122197  0.90894767  0.91138696\n",
      "   0.89338071  0.91725929  0.91369133  0.9392554   0.91993745  1.\n",
      "   0.94892826  0.98267161  0.90895184  0.84820522  0.83986801  0.79314977]\n",
      " [ 0.79712131  0.78475807  0.78079704  0.78154657  0.79273953  0.8048946\n",
      "   0.77267106  0.79856912  0.79738849  0.84509779  0.80891829  0.94892826\n",
      "   1.          0.96636494  0.85501782  0.7924829   0.87818485  0.711601  ]\n",
      " [ 0.8839502   0.87698988  0.87546299  0.87598655  0.8836597   0.89301333\n",
      "   0.86938135  0.8893305   0.88819994  0.91981806  0.89509928  0.98267161\n",
      "   0.96636494  1.          0.93013417  0.86212135  0.88232478  0.80437038]\n",
      " [ 0.9024741   0.90228274  0.90483929  0.90346169  0.90651708  0.9150118\n",
      "   0.90576106  0.90632368  0.91331439  0.9225725   0.91080202  0.90895184\n",
      "   0.85501782  0.93013417  1.          0.94711431  0.88754067  0.91038836]\n",
      " [ 0.90017665  0.8995782   0.90389581  0.90228656  0.90412669  0.91256583\n",
      "   0.90742296  0.89751955  0.90530461  0.9079813   0.89985734  0.84820522\n",
      "   0.7924829   0.86212135  0.94711431  1.          0.89008015  0.97106502]\n",
      " [ 0.76947228  0.76226611  0.76199711  0.76227065  0.76750741  0.78646305\n",
      "   0.76416323  0.77056553  0.78130621  0.81052132  0.78091393  0.83986801\n",
      "   0.87818485  0.88232478  0.88754067  0.89008015  1.          0.82015563]\n",
      " [ 0.90644409  0.9087494   0.91417153  0.9120898   0.91086129  0.91586182\n",
      "   0.91439593  0.89542918  0.90901264  0.90168197  0.90082747  0.79314977\n",
      "   0.711601    0.80437038  0.91038836  0.97106502  0.82015563  1.        ]]\n",
      "[[ 0.86541966  0.85835097  0.86300048  0.85718793  0.81008325  0.71838191\n",
      "   0.70871957  0.82561778  0.82791836  0.83378746  0.84672359  0.77155583\n",
      "   0.82534014  0.79057386  0.75157292  0.2286535   0.65189612  0.13436099]\n",
      " [ 1.          0.99268935  0.99474239  0.98181767  0.9196118   0.79804949\n",
      "   0.78986476  0.9431174   0.95035861  0.96267898  0.98307382  0.89338065\n",
      "   0.9489856   0.90725733  0.85472004  0.26254576  0.75059304  0.15295933]\n",
      " [ 0.99268935  1.          0.99454849  0.98096023  0.92624642  0.79254512\n",
      "   0.79132103  0.92882689  0.93656593  0.95443121  0.98330963  0.91450944\n",
      "   0.94826049  0.92882761  0.8440586   0.28556425  0.75428099  0.15532371]\n",
      " [ 0.99474239  0.99454849  1.          0.99076376  0.94068669  0.83118938\n",
      "   0.82215634  0.94673912  0.95395239  0.96398799  0.97953182  0.89762139\n",
      "   0.94842741  0.91677079  0.84401449  0.26568269  0.73912891  0.1528818 ]\n",
      " [ 0.98181767  0.98096023  0.99076376  1.          0.96432605  0.86834567\n",
      "   0.8552924   0.95211614  0.95099673  0.95447937  0.96919363  0.88668542\n",
      "   0.93781329  0.91017808  0.82527763  0.25934606  0.717735    0.15186698]\n",
      " [ 0.9196118   0.92624642  0.94068669  0.96432605  1.          0.93831467\n",
      "   0.90884257  0.9045663   0.89281936  0.89451864  0.91032367  0.86154928\n",
      "   0.8861991   0.86973471  0.77473804  0.25772067  0.68628729  0.14544508]\n",
      " [ 0.79804949  0.79254512  0.83118938  0.86834567  0.93831467  1.\n",
      "   0.94617734  0.86211658  0.82349238  0.79042738  0.77788225  0.70685371\n",
      "   0.76847163  0.73925147  0.66583068  0.19833019  0.54384504  0.12086249]\n",
      " [ 0.78986476  0.79132103  0.82215634  0.8552924   0.90884257  0.94617734\n",
      "   1.          0.85401131  0.80749167  0.78419733  0.78060279  0.73161187\n",
      "   0.75856711  0.73998825  0.66394494  0.21361589  0.57025466  0.12284179]\n",
      " [ 0.9431174   0.92882689  0.94673912  0.95211614  0.9045663   0.86211658\n",
      "   0.85401131  1.          0.97163471  0.94540179  0.92735431  0.83260965\n",
      "   0.90710639  0.85565946  0.7978992   0.25303597  0.66370217  0.14178447]\n",
      " [ 0.95035861  0.93656593  0.95395239  0.95099673  0.89281936  0.82349238\n",
      "   0.80749167  0.97163471  1.          0.98200467  0.93869746  0.84152949\n",
      "   0.9166905   0.86060369  0.81697298  0.25016581  0.67784296  0.14609196]\n",
      " [ 0.96267898  0.95443121  0.96398799  0.95447937  0.89451864  0.79042738\n",
      "   0.78419733  0.94540179  0.98200467  1.          0.96460538  0.89020211\n",
      "   0.93949529  0.89391343  0.83399552  0.25844923  0.72092261  0.15371085]\n",
      " [ 0.98307382  0.98330963  0.97953182  0.96919363  0.91032367  0.77788225\n",
      "   0.78060279  0.92735431  0.93869746  0.96460538  1.          0.92713835\n",
      "   0.95490935  0.92941558  0.84310806  0.27153081  0.75387772  0.15997767]\n",
      " [ 0.89338065  0.91450944  0.89762139  0.88668542  0.86154928  0.70685371\n",
      "   0.73161187  0.83260965  0.84152949  0.89020211  0.92713835  1.\n",
      "   0.94299859  0.9506626   0.81928822  0.32344909  0.7789612   0.14716193]\n",
      " [ 0.9489856   0.94826049  0.94842741  0.93781329  0.8861991   0.76847163\n",
      "   0.75856711  0.90710639  0.9166905   0.93949529  0.95490935  0.94299859\n",
      "   1.          0.96746579  0.86665315  0.27700692  0.75870575  0.15255914]\n",
      " [ 0.90725733  0.92882761  0.91677079  0.91017808  0.86973471  0.73925147\n",
      "   0.73998825  0.85565946  0.86060369  0.89391343  0.92941558  0.9506626\n",
      "   0.96746579  1.          0.82992633  0.29592508  0.76594554  0.15644504]\n",
      " [ 0.85472004  0.8440586   0.84401449  0.82527763  0.77473804  0.66583068\n",
      "   0.66394494  0.7978992   0.81697298  0.83399552  0.84310806  0.81928822\n",
      "   0.86665315  0.82992633  1.          0.28694358  0.91335449  0.17186285]\n",
      " [ 0.26254576  0.28556425  0.26568269  0.25934606  0.25772067  0.19833019\n",
      "   0.21361589  0.25303597  0.25016581  0.25844923  0.27153081  0.32344909\n",
      "   0.27700692  0.29592508  0.28694358  1.          0.36256217  0.9172875 ]\n",
      " [ 0.75059304  0.75428099  0.73912891  0.717735    0.68628729  0.54384504\n",
      "   0.57025466  0.66370217  0.67784296  0.72092261  0.75387772  0.7789612\n",
      "   0.75870575  0.76594554  0.91335449  0.36256217  1.          0.24896141]\n",
      " [ 0.15295933  0.15532371  0.1528818   0.15186698  0.14544508  0.12086249\n",
      "   0.12284179  0.14178447  0.14609196  0.15371085  0.15997767  0.14716193\n",
      "   0.15255914  0.15644504  0.17186285  0.9172875   0.24896141  1.        ]]\n",
      "[[ 0.99887291  0.99720509  0.99731382  0.99716031  0.9956962   0.98971051\n",
      "   0.99241339  0.99005321  0.98877667  0.99454728  0.99500365  0.9762174\n",
      "   0.95874601  0.95949553  0.95371437  0.76275604  0.93824977  0.76758777]\n",
      " [ 1.          0.99828196  0.99809178  0.9978362   0.99667436  0.99097938\n",
      "   0.99244841  0.99026721  0.99067164  0.99427111  0.99424997  0.97356041\n",
      "   0.95739198  0.95692461  0.9495586   0.7734353   0.94491944  0.77697481]\n",
      " [ 0.99828196  1.          0.99871364  0.99785572  0.99728406  0.99156323\n",
      "   0.99302951  0.98949609  0.98745515  0.99557927  0.99361464  0.97221852\n",
      "   0.95645997  0.95388803  0.95626209  0.76208613  0.94855391  0.76358541]\n",
      " [ 0.99809178  0.99871364  1.          0.99910495  0.99906271  0.99422893\n",
      "   0.99225511  0.9892575   0.98763695  0.99509164  0.99442277  0.97487315\n",
      "   0.9575444   0.95672113  0.95873525  0.77035416  0.94477884  0.7668833 ]\n",
      " [ 0.9978362   0.99785572  0.99910495  1.          0.99918246  0.9925749\n",
      "   0.9926231   0.98717614  0.98644276  0.99514129  0.99474949  0.97650095\n",
      "   0.96160009  0.96099551  0.95696292  0.76691429  0.9479841   0.76905924]\n",
      " [ 0.99667436  0.99728406  0.99906271  0.99918246  1.          0.99465719\n",
      "   0.99073924  0.98676424  0.98540424  0.99373431  0.99311802  0.97307894\n",
      "   0.9571353   0.95599104  0.95521695  0.77193125  0.94480999  0.76692726]\n",
      " [ 0.99097938  0.99156323  0.99422893  0.9925749   0.99465719  1.\n",
      "   0.98333723  0.97967666  0.97954033  0.98599109  0.98669569  0.96958247\n",
      "   0.94686421  0.9447269   0.95832181  0.79008931  0.92935577  0.75219318]\n",
      " [ 0.99244841  0.99302951  0.99225511  0.9926231   0.99073924  0.98333723\n",
      "   1.          0.99239178  0.98968336  0.99352459  0.99072306  0.96745194\n",
      "   0.95320764  0.95028811  0.94705316  0.77311489  0.94560949  0.7871488 ]\n",
      " [ 0.99026721  0.98949609  0.9892575   0.98717614  0.98676424  0.97967666\n",
      "   0.99239178  1.          0.99693199  0.99106876  0.98830416  0.95760582\n",
      "   0.94332014  0.94104596  0.93213382  0.7714533   0.94215256  0.789074  ]\n",
      " [ 0.99067164  0.98745515  0.98763695  0.98644276  0.98540424  0.97954033\n",
      "   0.98968336  0.99693199  1.          0.98916285  0.98731081  0.95682022\n",
      "   0.94312264  0.94074108  0.92378143  0.79130936  0.9475238   0.80949464]\n",
      " [ 0.99427111  0.99557927  0.99509164  0.99514129  0.99373431  0.98599109\n",
      "   0.99352459  0.99106876  0.98916285  1.          0.99805518  0.97693355\n",
      "   0.95794181  0.95555157  0.94872589  0.75469326  0.94666882  0.77292159]\n",
      " [ 0.99424997  0.99361464  0.99442277  0.99474949  0.99311802  0.98669569\n",
      "   0.99072306  0.98830416  0.98731081  0.99805518  1.          0.98348595\n",
      "   0.96277285  0.96264625  0.95131317  0.76069858  0.94308862  0.77474151]\n",
      " [ 0.97356041  0.97221852  0.97487315  0.97650095  0.97307894  0.96958247\n",
      "   0.96745194  0.95760582  0.95682022  0.97693355  0.98348595  1.\n",
      "   0.97444737  0.97612085  0.96125228  0.73264963  0.91720406  0.73555464]\n",
      " [ 0.95739198  0.95645997  0.9575444   0.96160009  0.9571353   0.94686421\n",
      "   0.95320764  0.94332014  0.94312264  0.95794181  0.96277285  0.97444737\n",
      "   1.          0.99463606  0.93629971  0.71959616  0.94052179  0.740977  ]\n",
      " [ 0.95692461  0.95388803  0.95672113  0.96099551  0.95599104  0.9447269\n",
      "   0.95028811  0.94104596  0.94074108  0.95555157  0.96264625  0.97612085\n",
      "   0.99463606  1.          0.94089916  0.71365392  0.93092835  0.74367251]\n",
      " [ 0.9495586   0.95626209  0.95873525  0.95696292  0.95521695  0.95832181\n",
      "   0.94705316  0.93213382  0.92378143  0.94872589  0.95131317  0.96125228\n",
      "   0.93629971  0.94089916  1.          0.7040972   0.90199435  0.66763187]\n",
      " [ 0.7734353   0.76208613  0.77035416  0.76691429  0.77193125  0.79008931\n",
      "   0.77311489  0.7714533   0.79130936  0.75469326  0.76069858  0.73264963\n",
      "   0.71959616  0.71365392  0.7040972   1.          0.75062156  0.84536312]\n",
      " [ 0.94491944  0.94855391  0.94477884  0.9479841   0.94480999  0.92935577\n",
      "   0.94560949  0.94215256  0.9475238   0.94666882  0.94308862  0.91720406\n",
      "   0.94052179  0.93092835  0.90199435  0.75062156  1.          0.76829443]\n",
      " [ 0.77697481  0.76358541  0.7668833   0.76905924  0.76692726  0.75219318\n",
      "   0.7871488   0.789074    0.80949464  0.77292159  0.77474151  0.73555464\n",
      "   0.740977    0.74367251  0.66763187  0.84536312  0.76829443  1.        ]]\n",
      "[[ 0.99918609  0.99836308  0.99663822  0.99775898  0.99777827  0.99174151\n",
      "   0.99416812  0.99562981  0.99051561  0.9742109   0.9915835   0.96602203\n",
      "   0.97513759  0.98618238  0.91091156  0.81681079  0.89515592  0.91550676]\n",
      " [ 1.          0.99929411  0.9957967   0.998091    0.99557993  0.98927533\n",
      "   0.99113942  0.99479241  0.98614062  0.96757897  0.98758522  0.96081864\n",
      "   0.97107198  0.98267526  0.90820226  0.80146747  0.89402248  0.91631693]\n",
      " [ 0.99929411  1.          0.99699702  0.99944019  0.99320758  0.98608129\n",
      "   0.98871548  0.99378195  0.98809536  0.97068652  0.98841461  0.96237051\n",
      "   0.9707721   0.98338858  0.90607102  0.81246455  0.89117905  0.91370498]\n",
      " [ 0.9957967   0.99699702  1.          0.99687766  0.99020203  0.98053766\n",
      "   0.98375954  0.99767417  0.99534726  0.97051378  0.99367384  0.96960195\n",
      "   0.97386709  0.98986002  0.90743104  0.82109148  0.88711606  0.91429861]\n",
      " [ 0.998091    0.99944019  0.99687766  1.          0.99251393  0.98433488\n",
      "   0.9889225   0.99229486  0.98959956  0.97514793  0.98813594  0.96031232\n",
      "   0.9678919   0.98172938  0.89793577  0.82184002  0.88186688  0.90363366]\n",
      " [ 0.99557993  0.99320758  0.99020203  0.99251393  1.          0.99715517\n",
      "   0.99838219  0.99029279  0.9864051   0.97703261  0.9894952   0.96524687\n",
      "   0.97635397  0.98307993  0.91423248  0.82411181  0.9019799   0.91576494]\n",
      " [ 0.98927533  0.98608129  0.98053766  0.98433488  0.99715517  1.\n",
      "   0.99642564  0.9807558   0.97673931  0.97347368  0.98513384  0.968523\n",
      "   0.9812556   0.98043603  0.93168152  0.82882036  0.92121269  0.92802344]\n",
      " [ 0.99113942  0.98871548  0.98375954  0.9889225   0.99838219  0.99642564\n",
      "   1.          0.98243065  0.98212987  0.9822064   0.98463742  0.95884331\n",
      "   0.97091885  0.97585493  0.90485914  0.83690825  0.89564567  0.90346518]\n",
      " [ 0.99479241  0.99378195  0.99767417  0.99229486  0.99029279  0.9807558\n",
      "   0.98243065  1.          0.99133379  0.95889675  0.99083355  0.96572366\n",
      "   0.97203341  0.98846771  0.90943509  0.79318662  0.88718277  0.91781875]\n",
      " [ 0.98614062  0.98809536  0.99534726  0.98959956  0.9864051   0.97673931\n",
      "   0.98212987  0.99133379  1.          0.98202118  0.99636373  0.97570759\n",
      "   0.97585664  0.99124067  0.90491677  0.86203506  0.88493022  0.90642792]\n",
      " [ 0.96757897  0.97068652  0.97051378  0.97514793  0.97703261  0.97347368\n",
      "   0.9822064   0.95889675  0.98202118  1.          0.9792449   0.95990408\n",
      "   0.96200345  0.96759398  0.88496551  0.91746446  0.87683527  0.87548438]\n",
      " [ 0.98758522  0.98841461  0.99367384  0.98813594  0.9894952   0.98513384\n",
      "   0.98463742  0.99083355  0.99636373  0.9792449   1.          0.98882063\n",
      "   0.99011952  0.99778229  0.93492167  0.86087353  0.91616741  0.93355839]\n",
      " [ 0.96081864  0.96237051  0.96960195  0.96031232  0.96524687  0.968523\n",
      "   0.95884331  0.96572366  0.97570759  0.95990408  0.98882063  1.\n",
      "   0.99707733  0.99337783  0.9694993   0.87686244  0.94904531  0.95937427]\n",
      " [ 0.97107198  0.9707721   0.97386709  0.9678919   0.97635397  0.9812556\n",
      "   0.97091885  0.97203341  0.97585664  0.96200345  0.99011952  0.99707733\n",
      "   1.          0.99410048  0.97396561  0.86012618  0.95580669  0.96588431]\n",
      " [ 0.98267526  0.98338858  0.98986002  0.98172938  0.98307993  0.98043603\n",
      "   0.97585493  0.98846771  0.99124067  0.96759398  0.99778229  0.99337783\n",
      "   0.99410048  1.          0.95134765  0.85027112  0.92878101  0.9482985 ]\n",
      " [ 0.90820226  0.90607102  0.90743104  0.89793577  0.91423248  0.93168152\n",
      "   0.90485914  0.90943509  0.90491677  0.88496551  0.93492167  0.9694993\n",
      "   0.97396561  0.95134765  1.          0.80033871  0.97956631  0.9838951 ]\n",
      " [ 0.80146747  0.81246455  0.82109148  0.82184002  0.82411181  0.82882036\n",
      "   0.83690825  0.79318662  0.86203506  0.91746446  0.86087353  0.87686244\n",
      "   0.86012618  0.85027112  0.80033871  1.          0.80455292  0.77018444]\n",
      " [ 0.89402248  0.89117905  0.88711606  0.88186688  0.9019799   0.92121269\n",
      "   0.89564567  0.88718277  0.88493022  0.87683527  0.91616741  0.94904531\n",
      "   0.95580669  0.92878101  0.97956631  0.80455292  1.          0.98475311]\n",
      " [ 0.91631693  0.91370498  0.91429861  0.90363366  0.91576494  0.92802344\n",
      "   0.90346518  0.91781875  0.90642792  0.87548438  0.93355839  0.95937427\n",
      "   0.96588431  0.9482985   0.9838951   0.77018444  0.98475311  1.        ]]\n",
      "[[ 0.98611242  0.97451457  0.9227465   0.90411096  0.86563784  0.87514169\n",
      "   0.86299068  0.85082453  0.84445664  0.83350719  0.79778026  0.77827566\n",
      "   0.41798326  0.29922996  0.78071044  0.48702348  0.35416607  0.21477954\n",
      "   0.21766572]\n",
      " [ 1.          0.9811599   0.93552818  0.91859054  0.88658862  0.88766499\n",
      "   0.87292618  0.8638875   0.85459978  0.84411886  0.80423941  0.77577602\n",
      "   0.42559267  0.29894191  0.7732584   0.48975913  0.34563387  0.21604805\n",
      "   0.21675121]\n",
      " [ 0.9811599   1.          0.95245766  0.934647    0.90879174  0.90992758\n",
      "   0.89484004  0.89315452  0.88306407  0.87314247  0.84351561  0.81852078\n",
      "   0.43504242  0.30243919  0.81327331  0.50264273  0.35996785  0.21902377\n",
      "   0.22598594]\n",
      " [ 0.93552818  0.95245766  1.          0.98556014  0.97231269  0.94105887\n",
      "   0.93850432  0.93285604  0.91670115  0.89824711  0.87272999  0.84304008\n",
      "   0.46190199  0.33923364  0.82841098  0.51091387  0.38881498  0.23694787\n",
      "   0.24479195]\n",
      " [ 0.91859054  0.934647    0.98556014  1.          0.9855748   0.9369277\n",
      "   0.92945283  0.92464476  0.91271766  0.89959365  0.8822265   0.8363959\n",
      "   0.43984387  0.35528596  0.81684084  0.51198792  0.37953366  0.22861239\n",
      "   0.24439654]\n",
      " [ 0.88658862  0.90879174  0.97231269  0.9855748   1.          0.9506554\n",
      "   0.93989362  0.93555913  0.92498959  0.9049508   0.88533549  0.82608451\n",
      "   0.45158148  0.35588565  0.80234038  0.51325397  0.38838888  0.24012798\n",
      "   0.24791277]\n",
      " [ 0.88766499  0.90992758  0.94105887  0.9369277   0.9506554   1.\n",
      "   0.98286431  0.96166239  0.93391683  0.91290609  0.90120797  0.85206422\n",
      "   0.47840203  0.36301027  0.81489812  0.54879119  0.4078789   0.2554168\n",
      "   0.26511088]\n",
      " [ 0.87292618  0.89484004  0.93850432  0.92945283  0.93989362  0.98286431\n",
      "   1.          0.97834944  0.94146706  0.91202167  0.89532076  0.85514755\n",
      "   0.49316359  0.36606946  0.81022759  0.53038241  0.40027203  0.24640053\n",
      "   0.25673426]\n",
      " [ 0.8638875   0.89315452  0.93285604  0.92464476  0.93555913  0.96166239\n",
      "   0.97834944  1.          0.97021116  0.94363261  0.90682753  0.87205103\n",
      "   0.49939996  0.36636872  0.82622315  0.53351938  0.41494097  0.27209952\n",
      "   0.28325043]\n",
      " [ 0.85459978  0.88306407  0.91670115  0.91271766  0.92498959  0.93391683\n",
      "   0.94146706  0.97021116  1.          0.97342055  0.93168047  0.87595807\n",
      "   0.50238087  0.36530407  0.82259472  0.54379183  0.41579803  0.26797909\n",
      "   0.27693482]\n",
      " [ 0.84411886  0.87314247  0.89824711  0.89959365  0.9049508   0.91290609\n",
      "   0.91202167  0.94363261  0.97342055  1.          0.93781416  0.8801972\n",
      "   0.48406764  0.35754556  0.81960625  0.53963756  0.41304843  0.26329423\n",
      "   0.2748076 ]\n",
      " [ 0.80423941  0.84351561  0.87272999  0.8822265   0.88533549  0.90120797\n",
      "   0.89532076  0.90682753  0.93168047  0.93781416  1.          0.92794283\n",
      "   0.4353825   0.36482773  0.83837018  0.56222964  0.39142873  0.24671392\n",
      "   0.25895729]\n",
      " [ 0.77577602  0.81852078  0.84304008  0.8363959   0.82608451  0.85206422\n",
      "   0.85514755  0.87205103  0.87595807  0.8801972   0.92794283  1.\n",
      "   0.34978896  0.31487435  0.88578694  0.50342713  0.29174976  0.19663517\n",
      "   0.19967675]\n",
      " [ 0.42559267  0.43504242  0.46190199  0.43984387  0.45158148  0.47840203\n",
      "   0.49316359  0.49939996  0.50238087  0.48406764  0.4353825   0.34978896\n",
      "   1.          0.54358061  0.41938058  0.38873992  0.69205512  0.57815061\n",
      "   0.60365987]\n",
      " [ 0.29894191  0.30243919  0.33923364  0.35528596  0.35588565  0.36301027\n",
      "   0.36606946  0.36636872  0.36530407  0.35754556  0.36482773  0.31487435\n",
      "   0.54358061  1.          0.33232988  0.3193015   0.48194065  0.39106866\n",
      "   0.40507648]\n",
      " [ 0.7732584   0.81327331  0.82841098  0.81684084  0.80234038  0.81489812\n",
      "   0.81022759  0.82622315  0.82259472  0.81960625  0.83837018  0.88578694\n",
      "   0.41938058  0.33232988  1.          0.5972312   0.35288569  0.22466535\n",
      "   0.25015172]\n",
      " [ 0.48975913  0.50264273  0.51091387  0.51198792  0.51325397  0.54879119\n",
      "   0.53038241  0.53351938  0.54379183  0.53963756  0.56222964  0.50342713\n",
      "   0.38873992  0.3193015   0.5972312   1.          0.35015561  0.25285066\n",
      "   0.27124241]\n",
      " [ 0.34563387  0.35996785  0.38881498  0.37953366  0.38838888  0.4078789\n",
      "   0.40027203  0.41494097  0.41579803  0.41304843  0.39142873  0.29174976\n",
      "   0.69205512  0.48194065  0.35288569  0.35015561  1.          0.77858458\n",
      "   0.81131341]\n",
      " [ 0.21604805  0.21902377  0.23694787  0.22861239  0.24012798  0.2554168\n",
      "   0.24640053  0.27209952  0.26797909  0.26329423  0.24671392  0.19663517\n",
      "   0.57815061  0.39106866  0.22466535  0.25285066  0.77858458  1.\n",
      "   0.94041627]\n",
      " [ 0.21675121  0.22598594  0.24479195  0.24439654  0.24791277  0.26511088\n",
      "   0.25673426  0.28325043  0.27693482  0.2748076   0.25895729  0.19967675\n",
      "   0.60365987  0.40507648  0.25015172  0.27124241  0.81131341  0.94041627\n",
      "   1.        ]]\n",
      "[[ 0.99556953  0.98643011  0.98150311  0.9833957   0.9793763   0.97014723\n",
      "   0.98036825  0.98603662  0.97808212  0.97698061  0.95555279  0.96435506\n",
      "   0.42292382  0.53807001  0.87224754  0.81705193  0.27341559  0.19686915\n",
      "   0.26961462]\n",
      " [ 1.          0.99515118  0.9867062   0.98652303  0.98672602  0.98194559\n",
      "   0.9842694   0.98173062  0.9759301   0.97828309  0.96670646  0.97116845\n",
      "   0.41551331  0.54615906  0.89957761  0.84799172  0.27941757  0.19743865\n",
      "   0.27303544]\n",
      " [ 0.99515118  1.          0.98951564  0.98708594  0.98912137  0.98660782\n",
      "   0.98502291  0.97521842  0.96656676  0.97180199  0.97260349  0.97288937\n",
      "   0.41194025  0.5414126   0.91926758  0.87252261  0.28571456  0.19914807\n",
      "   0.27852586]\n",
      " [ 0.9867062   0.98951564  1.          0.99117153  0.98754869  0.97837159\n",
      "   0.98360208  0.97789081  0.95379742  0.95723682  0.97475962  0.97183874\n",
      "   0.4371365   0.53927129  0.9163692   0.87474284  0.30680273  0.21603841\n",
      "   0.29676562]\n",
      " [ 0.98652303  0.98708594  0.99117153  1.          0.99433876  0.97997865\n",
      "   0.98896228  0.98262983  0.96877584  0.97110116  0.96935032  0.97387794\n",
      "   0.43594929  0.56353152  0.88778133  0.84084926  0.31137344  0.21508018\n",
      "   0.30132932]\n",
      " [ 0.98672602  0.98912137  0.98754869  0.99433876  1.          0.99124583\n",
      "   0.99421907  0.97864576  0.96888196  0.97120405  0.96544207  0.96946308\n",
      "   0.43500164  0.56812353  0.90282297  0.85517368  0.30795934  0.21133879\n",
      "   0.28963881]\n",
      " [ 0.98194559  0.98660782  0.97837159  0.97997865  0.99124583  1.\n",
      "   0.99324746  0.96820989  0.96640037  0.96925526  0.95676186  0.9561201\n",
      "   0.4105934   0.55434575  0.917992    0.87049154  0.30030912  0.20781431\n",
      "   0.28053684]\n",
      " [ 0.9842694   0.98502291  0.98360208  0.98896228  0.99421907  0.99324746\n",
      "   1.          0.98613928  0.97786568  0.97594721  0.95242543  0.9586479\n",
      "   0.4472804   0.56031675  0.8899452   0.83882903  0.30364906  0.20979402\n",
      "   0.28246245]\n",
      " [ 0.98173062  0.97521842  0.97789081  0.98262983  0.97864576  0.96820989\n",
      "   0.98613928  1.          0.98235697  0.97807275  0.94158724  0.95465606\n",
      "   0.447069    0.55781948  0.85234771  0.79057663  0.30292733  0.2051106\n",
      "   0.27731298]\n",
      " [ 0.9759301   0.96656676  0.95379742  0.96877584  0.96888196  0.96640037\n",
      "   0.97786568  0.98235697  1.          0.99631278  0.92512265  0.94795801\n",
      "   0.44199229  0.57268606  0.83643509  0.76875203  0.30592712  0.19093571\n",
      "   0.26854499]\n",
      " [ 0.97828309  0.97180199  0.95723682  0.97110116  0.97120405  0.96925526\n",
      "   0.97594721  0.97807275  0.99631278  1.          0.93838089  0.9561198\n",
      "   0.41977391  0.58150205  0.85245359  0.79155113  0.31950778  0.19161553\n",
      "   0.27048803]\n",
      " [ 0.96670646  0.97260349  0.97475962  0.96935032  0.96544207  0.95676186\n",
      "   0.95242543  0.94158724  0.92512265  0.93838089  1.          0.98817666\n",
      "   0.39294905  0.51308525  0.93601592  0.91144303  0.30704906  0.20381623\n",
      "   0.27599246]\n",
      " [ 0.97116845  0.97288937  0.97183874  0.97387794  0.96946308  0.9561201\n",
      "   0.9586479   0.95465606  0.94795801  0.9561198   0.98817666  1.\n",
      "   0.44252976  0.52472424  0.91053012  0.8748077   0.33656168  0.21701678\n",
      "   0.29250415]\n",
      " [ 0.41551331  0.41194025  0.4371365   0.43594929  0.43500164  0.4105934\n",
      "   0.4472804   0.447069    0.44199229  0.41977391  0.39294905  0.44252976\n",
      "   1.          0.53453792  0.36489354  0.36623633  0.5028645   0.31492645\n",
      "   0.42771774]\n",
      " [ 0.54615906  0.5414126   0.53927129  0.56353152  0.56812353  0.55434575\n",
      "   0.56031675  0.55781948  0.57268606  0.58150205  0.51308525  0.52472424\n",
      "   0.53453792  1.          0.51097947  0.4535941   0.53454111  0.30872281\n",
      "   0.42860685]\n",
      " [ 0.89957761  0.91926758  0.9163692   0.88778133  0.90282297  0.917992\n",
      "   0.8899452   0.85234771  0.83643509  0.85245359  0.93601592  0.91053012\n",
      "   0.36489354  0.51097947  1.          0.96694557  0.29887968  0.19401875\n",
      "   0.26817154]\n",
      " [ 0.84799172  0.87252261  0.87474284  0.84084926  0.85517368  0.87049154\n",
      "   0.83882903  0.79057663  0.76875203  0.79155113  0.91144303  0.8748077\n",
      "   0.36623633  0.4535941   0.96694557  1.          0.31931986  0.22567569\n",
      "   0.30699953]\n",
      " [ 0.27941757  0.28571456  0.30680273  0.31137344  0.30795934  0.30030912\n",
      "   0.30364906  0.30292733  0.30592712  0.31950778  0.30704906  0.33656168\n",
      "   0.5028645   0.53454111  0.29887968  0.31931986  1.          0.45171174\n",
      "   0.66476893]\n",
      " [ 0.19743865  0.19914807  0.21603841  0.21508018  0.21133879  0.20781431\n",
      "   0.20979402  0.2051106   0.19093571  0.19161553  0.20381623  0.21701678\n",
      "   0.31492645  0.30872281  0.19401875  0.22567569  0.45171174  1.\n",
      "   0.64139226]\n",
      " [ 0.27303544  0.27852586  0.29676562  0.30132932  0.28963881  0.28053684\n",
      "   0.28246245  0.27731298  0.26854499  0.27048803  0.27599246  0.29250415\n",
      "   0.42771774  0.42860685  0.26817154  0.30699953  0.66476893  0.64139226\n",
      "   1.        ]]\n",
      "[[ 0.9917634   0.99046742  0.9698947   0.96873148  0.96980923  0.97567919\n",
      "   0.97390027  0.96345199  0.96235533  0.96778486  0.95877129  0.92915896\n",
      "   0.85342525  0.80155607  0.89929495  0.87965075  0.83957179  0.68798244\n",
      "   0.6935583 ]\n",
      " [ 1.          0.99481183  0.97964164  0.97946029  0.97724606  0.97992578\n",
      "   0.97054685  0.96500613  0.96797521  0.97530233  0.9684419   0.94097582\n",
      "   0.82755841  0.82815471  0.91397009  0.88263842  0.83200051  0.71457414\n",
      "   0.71191383]\n",
      " [ 0.99481183  1.          0.97317003  0.97363445  0.9740951   0.97620286\n",
      "   0.96374584  0.95495097  0.96019979  0.96920246  0.96647554  0.93618821\n",
      "   0.84513911  0.83664623  0.89767556  0.87936536  0.84111173  0.722866\n",
      "   0.72167944]\n",
      " [ 0.97964164  0.97317003  1.          0.99727359  0.99281099  0.98237041\n",
      "   0.97692898  0.97879943  0.97183388  0.96638956  0.96250465  0.92108813\n",
      "   0.81428068  0.80618362  0.93029884  0.88352065  0.8307902   0.73783405\n",
      "   0.72292997]\n",
      " [ 0.97946029  0.97363445  0.99727359  1.          0.99580401  0.98561548\n",
      "   0.97684035  0.97828373  0.97577707  0.97373795  0.9697525   0.93060278\n",
      "   0.81177419  0.82012338  0.9323104   0.88754927  0.82288395  0.74075138\n",
      "   0.71723552]\n",
      " [ 0.97724606  0.9740951   0.99281099  0.99580401  1.          0.99027904\n",
      "   0.97857208  0.97725907  0.97594109  0.9736386   0.97190298  0.93312231\n",
      "   0.82596962  0.81897257  0.92807882  0.88975831  0.83012957  0.73478551\n",
      "   0.71030779]\n",
      " [ 0.97992578  0.97620286  0.98237041  0.98561548  0.99027904  1.\n",
      "   0.98550768  0.9816627   0.98270077  0.9815508   0.97595275  0.94930847\n",
      "   0.82570713  0.81835714  0.9173336   0.88341428  0.81112721  0.69800326\n",
      "   0.68143283]\n",
      " [ 0.97054685  0.96374584  0.97692898  0.97684035  0.97857208  0.98550768\n",
      "   1.          0.99653604  0.98982198  0.98034592  0.96241161  0.94125717\n",
      "   0.82911449  0.80050207  0.89759857  0.85871474  0.80220568  0.67278502\n",
      "   0.67039246]\n",
      " [ 0.96500613  0.95495097  0.97879943  0.97828373  0.97725907  0.9816627\n",
      "   0.99653604  1.          0.99226421  0.9790597   0.96040393  0.93867965\n",
      "   0.81036799  0.79753714  0.90668565  0.85804327  0.79606164  0.68086628\n",
      "   0.67593442]\n",
      " [ 0.96797521  0.96019979  0.97183388  0.97577707  0.97594109  0.98270077\n",
      "   0.98982198  0.99226421  1.          0.99258631  0.97736871  0.96019215\n",
      "   0.80926829  0.83233138  0.91505183  0.87100177  0.79319575  0.69418785\n",
      "   0.68348384]\n",
      " [ 0.97530233  0.96920246  0.96638956  0.97373795  0.9736386   0.9815508\n",
      "   0.98034592  0.9790597   0.99258631  1.          0.98916135  0.96662942\n",
      "   0.82306106  0.86677524  0.92121572  0.8896366   0.80630576  0.70640499\n",
      "   0.69244097]\n",
      " [ 0.9684419   0.96647554  0.96250465  0.9697525   0.97190298  0.97595275\n",
      "   0.96241161  0.96040393  0.97736871  0.98916135  1.          0.97065875\n",
      "   0.83747061  0.88011803  0.93307114  0.90805401  0.83632432  0.73450108\n",
      "   0.72178119]\n",
      " [ 0.94097582  0.93618821  0.92108813  0.93060278  0.93312231  0.94930847\n",
      "   0.94125717  0.93867965  0.96019215  0.96662942  0.97065875  1.\n",
      "   0.78802888  0.85151314  0.88789696  0.85566769  0.78133057  0.69082369\n",
      "   0.68160913]\n",
      " [ 0.82755841  0.84513911  0.81428068  0.81177419  0.82596962  0.82570713\n",
      "   0.82911449  0.81036799  0.80926829  0.82306106  0.83747061  0.78802888\n",
      "   1.          0.82021833  0.78700894  0.84508378  0.8954532   0.74388065\n",
      "   0.76109723]\n",
      " [ 0.82815471  0.83664623  0.80618362  0.82012338  0.81897257  0.81835714\n",
      "   0.80050207  0.79753714  0.83233138  0.86677524  0.88011803  0.85151314\n",
      "   0.82021833  1.          0.78748045  0.81146182  0.76298428  0.78704467\n",
      "   0.76549685]\n",
      " [ 0.91397009  0.89767556  0.93029884  0.9323104   0.92807882  0.9173336\n",
      "   0.89759857  0.90668565  0.91505183  0.92121572  0.93307114  0.88789696\n",
      "   0.78700894  0.78748045  1.          0.95005278  0.86424875  0.72044886\n",
      "   0.70472309]\n",
      " [ 0.88263842  0.87936536  0.88352065  0.88754927  0.88975831  0.88341428\n",
      "   0.85871474  0.85804327  0.87100177  0.8896366   0.90805401  0.85566769\n",
      "   0.84508378  0.81146182  0.95005278  1.          0.89425827  0.73838145\n",
      "   0.73589755]\n",
      " [ 0.83200051  0.84111173  0.8307902   0.82288395  0.83012957  0.81112721\n",
      "   0.80220568  0.79606164  0.79319575  0.80630576  0.83632432  0.78133057\n",
      "   0.8954532   0.76298428  0.86424875  0.89425827  1.          0.79390579\n",
      "   0.8295972 ]\n",
      " [ 0.71457414  0.722866    0.73783405  0.74075138  0.73478551  0.69800326\n",
      "   0.67278502  0.68086628  0.69418785  0.70640499  0.73450108  0.69082369\n",
      "   0.74388065  0.78704467  0.72044886  0.73838145  0.79390579  1.\n",
      "   0.97429986]\n",
      " [ 0.71191383  0.72167944  0.72292997  0.71723552  0.71030779  0.68143283\n",
      "   0.67039246  0.67593442  0.68348384  0.69244097  0.72178119  0.68160913\n",
      "   0.76109723  0.76549685  0.70472309  0.73589755  0.8295972   0.97429986\n",
      "   1.        ]]\n",
      "[[ 0.99813561  0.99384018  0.97462316  0.97718584  0.98004065  0.9722529\n",
      "   0.97707052  0.96772971  0.95891042  0.95911012  0.92321338  0.9269215\n",
      "   0.44424132  0.84703075  0.91281565  0.52853431  0.51652954  0.53584648\n",
      "   0.535555  ]\n",
      " [ 1.          0.99405816  0.97486452  0.97652452  0.97970071  0.9736044\n",
      "   0.97820464  0.97047462  0.96005871  0.96140387  0.92509567  0.92835961\n",
      "   0.44605648  0.8533683   0.90705002  0.52684691  0.50569864  0.52421514\n",
      "   0.52373207]\n",
      " [ 0.99405816  1.          0.9627822   0.96714834  0.97671478  0.97231735\n",
      "   0.97184173  0.96541659  0.96025416  0.96113616  0.92383298  0.93726212\n",
      "   0.43141404  0.85744189  0.90530164  0.52976194  0.49210843  0.51885923\n",
      "   0.51892864]\n",
      " [ 0.97486452  0.9627822   1.          0.99176952  0.97824314  0.96663754\n",
      "   0.97416517  0.95960431  0.93911922  0.93993173  0.92021305  0.90552614\n",
      "   0.45544202  0.81173106  0.91553427  0.51547209  0.54165157  0.57424033\n",
      "   0.57173009]\n",
      " [ 0.97652452  0.96714834  0.99176952  1.          0.99103066  0.97990236\n",
      "   0.98491856  0.97191369  0.95586513  0.95493777  0.93922727  0.92689949\n",
      "   0.43845817  0.82377486  0.92426216  0.52432853  0.52359259  0.56022616\n",
      "   0.55805293]\n",
      " [ 0.97970071  0.97671478  0.97824314  0.99103066  1.          0.99196664\n",
      "   0.99274081  0.98785679  0.97464926  0.97379075  0.95244346  0.95420657\n",
      "   0.44676404  0.86272328  0.91150812  0.55435822  0.48077799  0.5023028\n",
      "   0.50123301]\n",
      " [ 0.9736044   0.97231735  0.96663754  0.97990236  0.99196664  1.\n",
      "   0.99489915  0.98842347  0.97445439  0.97735018  0.95892339  0.96320444\n",
      "   0.46254621  0.86415789  0.89993005  0.57600144  0.46643581  0.49163945\n",
      "   0.49116412]\n",
      " [ 0.97820464  0.97184173  0.97416517  0.98491856  0.99274081  0.99489915\n",
      "   1.          0.99013517  0.97402565  0.97632024  0.95569722  0.95432135\n",
      "   0.46393705  0.86097901  0.9007389   0.55931303  0.47923212  0.49209861\n",
      "   0.4922228 ]\n",
      " [ 0.97047462  0.96541659  0.95960431  0.97191369  0.98785679  0.98842347\n",
      "   0.99013517  1.          0.98824199  0.98568025  0.95999998  0.96779001\n",
      "   0.4336514   0.89033352  0.87813871  0.53772507  0.41211312  0.41507267\n",
      "   0.4147616 ]\n",
      " [ 0.96005871  0.96025416  0.93911922  0.95586513  0.97464926  0.97445439\n",
      "   0.97402565  0.98824199  1.          0.99227433  0.96784638  0.97599936\n",
      "   0.37704302  0.88620977  0.88239906  0.52328183  0.36626884  0.37496055\n",
      "   0.37399699]\n",
      " [ 0.96140387  0.96113616  0.93993173  0.95493777  0.97379075  0.97735018\n",
      "   0.97632024  0.98568025  0.99227433  1.          0.97611657  0.97286586\n",
      "   0.4220066   0.88370759  0.89442019  0.54845806  0.39680932  0.4090385\n",
      "   0.40945438]\n",
      " [ 0.92509567  0.92383298  0.92021305  0.93922727  0.95244346  0.95892339\n",
      "   0.95569722  0.95999998  0.96784638  0.97611657  1.          0.96820678\n",
      "   0.43784334  0.83928107  0.90368506  0.56260274  0.42109965  0.43281964\n",
      "   0.43241387]\n",
      " [ 0.92835961  0.93726212  0.90552614  0.92689949  0.95420657  0.96320444\n",
      "   0.95432135  0.96779001  0.97599936  0.97286586  0.96820678  1.\n",
      "   0.39061594  0.88842417  0.87210636  0.55256141  0.34324015  0.35494525\n",
      "   0.35505783]\n",
      " [ 0.44605648  0.43141404  0.45544202  0.43845817  0.44676404  0.46254621\n",
      "   0.46393705  0.4336514   0.37704302  0.4220066   0.43784334  0.39061594\n",
      "   1.          0.51572818  0.42439705  0.54749763  0.71964004  0.46457408\n",
      "   0.47056921]\n",
      " [ 0.8533683   0.85744189  0.81173106  0.82377486  0.86272328  0.86415789\n",
      "   0.86097901  0.89033352  0.88620977  0.88370759  0.83928107  0.88842417\n",
      "   0.51572818  1.          0.71800235  0.50551988  0.3634587   0.24533799\n",
      "   0.24750666]\n",
      " [ 0.90705002  0.90530164  0.91553427  0.92426216  0.91150812  0.89993005\n",
      "   0.9007389   0.87813871  0.88239906  0.89442019  0.90368506  0.87210636\n",
      "   0.42439705  0.71800235  1.          0.55438494  0.56680165  0.65870088\n",
      "   0.65645957]\n",
      " [ 0.52684691  0.52976194  0.51547209  0.52432853  0.55435822  0.57600144\n",
      "   0.55931303  0.53772507  0.52328183  0.54845806  0.56260274  0.55256141\n",
      "   0.54749763  0.50551988  0.55438494  1.          0.3569701   0.30739795\n",
      "   0.30378247]\n",
      " [ 0.50569864  0.49210843  0.54165157  0.52359259  0.48077799  0.46643581\n",
      "   0.47923212  0.41211312  0.36626884  0.39680932  0.42109965  0.34324015\n",
      "   0.71964004  0.3634587   0.56680165  0.3569701   1.          0.85235626\n",
      "   0.85147332]\n",
      " [ 0.52421514  0.51885923  0.57424033  0.56022616  0.5023028   0.49163945\n",
      "   0.49209861  0.41507267  0.37496055  0.4090385   0.43281964  0.35494525\n",
      "   0.46457408  0.24533799  0.65870088  0.30739795  0.85235626  1.\n",
      "   0.9972272 ]\n",
      " [ 0.52373207  0.51892864  0.57173009  0.55805293  0.50123301  0.49116412\n",
      "   0.4922228   0.4147616   0.37399699  0.40945438  0.43241387  0.35505783\n",
      "   0.47056921  0.24750666  0.65645957  0.30378247  0.85147332  0.9972272\n",
      "   1.        ]]\n",
      "[[ 0.99182441  0.984022    0.96958658  0.96901759  0.95963749  0.96301073\n",
      "   0.93972474  0.92819884  0.93127978  0.93733029  0.88832965  0.78908617\n",
      "   0.66932908  0.71060436  0.69431711  0.73360555  0.48469572  0.60740223\n",
      "   0.48744842]\n",
      " [ 1.          0.99219469  0.95451787  0.95431421  0.94943983  0.95016471\n",
      "   0.92060905  0.91204993  0.90994167  0.91751323  0.880213    0.77362264\n",
      "   0.64848911  0.71045741  0.67306588  0.70817744  0.49996953  0.59284811\n",
      "   0.47576854]\n",
      " [ 0.99219469  1.          0.9571418   0.95507515  0.9550926   0.95046177\n",
      "   0.92565569  0.92129483  0.92049312  0.91654075  0.87856721  0.77008169\n",
      "   0.6497176   0.70716259  0.66799972  0.68898519  0.50343822  0.56897004\n",
      "   0.45660607]\n",
      " [ 0.95451787  0.9571418   1.          0.99680042  0.98970714  0.98974632\n",
      "   0.98252504  0.9728268   0.97574226  0.98184531  0.95606794  0.88274088\n",
      "   0.66441991  0.73209579  0.76547002  0.74927677  0.51726252  0.63001267\n",
      "   0.5055936 ]\n",
      " [ 0.95431421  0.95507515  0.99680042  1.          0.98955383  0.98636667\n",
      "   0.98010336  0.97119653  0.97136895  0.97625475  0.94856768  0.87742513\n",
      "   0.67907594  0.73272076  0.76944947  0.75466948  0.52050531  0.62671993\n",
      "   0.50295113]\n",
      " [ 0.94943983  0.9550926   0.98970714  0.98955383  1.          0.99246991\n",
      "   0.9873339   0.98271031  0.98214072  0.98034831  0.96018709  0.89488179\n",
      "   0.67371828  0.71230891  0.77670506  0.74127509  0.48242181  0.56756096\n",
      "   0.45547526]\n",
      " [ 0.95016471  0.95046177  0.98974632  0.98636667  0.99246991  1.\n",
      "   0.99095039  0.98345787  0.97813175  0.98746629  0.96560428  0.89335322\n",
      "   0.68890118  0.75054654  0.77408957  0.76117264  0.50813591  0.62510392\n",
      "   0.50165426]\n",
      " [ 0.92060905  0.92565569  0.98252504  0.98010336  0.9873339   0.99095039\n",
      "   1.          0.99687182  0.98951979  0.98920918  0.96581287  0.91070042\n",
      "   0.72980408  0.74623806  0.78506347  0.75691569  0.51999863  0.62162573\n",
      "   0.49886297]\n",
      " [ 0.91204993  0.92129483  0.9728268   0.97119653  0.98271031  0.98345787\n",
      "   0.99687182  1.          0.98855483  0.98211686  0.95870064  0.90156745\n",
      "   0.7383903   0.7439536   0.78119978  0.74633448  0.53002739  0.61168504\n",
      "   0.49088543]\n",
      " [ 0.90994167  0.92049312  0.97574226  0.97136895  0.98214072  0.97813175\n",
      "   0.98951979  0.98855483  1.          0.98977388  0.96056783  0.89854472\n",
      "   0.68473734  0.71327161  0.76670157  0.72345691  0.47516166  0.570647\n",
      "   0.45795185]\n",
      " [ 0.91751323  0.91654075  0.98184531  0.97625475  0.98034831  0.98746629\n",
      "   0.98920918  0.98211686  0.98977388  1.          0.98089812  0.91857706\n",
      "   0.66779112  0.73929122  0.78901878  0.75747385  0.49290058  0.61984152\n",
      "   0.49743112]\n",
      " [ 0.880213    0.87856721  0.95606794  0.94856768  0.96018709  0.96560428\n",
      "   0.96581287  0.95870064  0.96056783  0.98089812  1.          0.9608677\n",
      "   0.59832357  0.71027776  0.8172902   0.74131463  0.49258447  0.58825441\n",
      "   0.47208204]\n",
      " [ 0.77362264  0.77008169  0.88274088  0.87742513  0.89488179  0.89335322\n",
      "   0.91070042  0.90156745  0.89854472  0.91857706  0.9608677   1.\n",
      "   0.54433664  0.56445844  0.85793188  0.72277804  0.37666522  0.47027127\n",
      "   0.37739899]\n",
      " [ 0.64848911  0.6497176   0.66441991  0.67907594  0.67371828  0.68890118\n",
      "   0.72980408  0.7383903   0.68473734  0.66779112  0.59832357  0.54433664\n",
      "   1.          0.74772823  0.48329247  0.5969234   0.61443707  0.63519977\n",
      "   0.50975632]\n",
      " [ 0.71045741  0.70716259  0.73209579  0.73272076  0.71230891  0.75054654\n",
      "   0.74623806  0.7439536   0.71327161  0.73929122  0.71027776  0.56445844\n",
      "   0.74772823  1.          0.47049691  0.60867213  0.7135371   0.81659638\n",
      "   0.65532952]\n",
      " [ 0.67306588  0.66799972  0.76547002  0.76944947  0.77670506  0.77408957\n",
      "   0.78506347  0.78119978  0.76670157  0.78901878  0.8172902   0.85793188\n",
      "   0.48329247  0.47049691  1.          0.92246862  0.36514463  0.38646628\n",
      "   0.31014436]\n",
      " [ 0.70817744  0.68898519  0.74927677  0.75466948  0.74127509  0.76117264\n",
      "   0.75691569  0.74633448  0.72345691  0.75747385  0.74131463  0.72277804\n",
      "   0.5969234   0.60867213  0.92246862  1.          0.4559234   0.51320044\n",
      "   0.41185023]\n",
      " [ 0.49996953  0.50343822  0.51726252  0.52050531  0.48242181  0.50813591\n",
      "   0.51999863  0.53002739  0.47516166  0.49290058  0.49258447  0.37666522\n",
      "   0.61443707  0.7135371   0.36514463  0.4559234   1.          0.69337257\n",
      "   0.55644077]\n",
      " [ 0.59284811  0.56897004  0.63001267  0.62671993  0.56756096  0.62510392\n",
      "   0.62162573  0.61168504  0.570647    0.61984152  0.58825441  0.47027127\n",
      "   0.63519977  0.81659638  0.38646628  0.51320044  0.69337257  1.\n",
      "   0.91340632]\n",
      " [ 0.47576854  0.45660607  0.5055936   0.50295113  0.45547526  0.50165426\n",
      "   0.49886297  0.49088543  0.45795185  0.49743112  0.47208204  0.37739899\n",
      "   0.50975632  0.65532952  0.31014436  0.41185023  0.55644077  0.91340632\n",
      "   1.        ]]\n",
      "[[ 0.97871826  0.94639124  0.98093708  0.97429212  0.94295686  0.98273999\n",
      "   0.9790105   0.95281045  0.9373459   0.92386121  0.93230414  0.9292742\n",
      "   0.59357603  0.56671759  0.88989587  0.85238752  0.57911265  0.23085994\n",
      "   0.14173113]\n",
      " [ 1.          0.98973546  0.99496296  0.99494494  0.98763684  0.98666996\n",
      "   0.96956123  0.98937819  0.98397672  0.97549993  0.98193134  0.97475611\n",
      "   0.59606087  0.62177022  0.81379834  0.82660696  0.57945703  0.22599514\n",
      "   0.12745831]\n",
      " [ 0.98973546  1.          0.98649602  0.99191601  0.99869415  0.96660397\n",
      "   0.93722995  0.98705945  0.98567604  0.9773781   0.98615298  0.97166879\n",
      "   0.60617541  0.59603924  0.77196983  0.82337942  0.59244522  0.23151363\n",
      "   0.12458994]\n",
      " [ 0.99496296  0.98649602  1.          0.99895667  0.98731649  0.99291278\n",
      "   0.97645139  0.98881239  0.97955084  0.9692281   0.9777914   0.96862428\n",
      "   0.58026883  0.58172259  0.84909357  0.85200436  0.56497923  0.19254855\n",
      "   0.09252925]\n",
      " [ 0.99494494  0.99191601  0.99895667  1.          0.99243089  0.9871542\n",
      "   0.9663628   0.9886831   0.98070325  0.97001318  0.97960674  0.96765948\n",
      "   0.58762326  0.57060561  0.83718887  0.85545685  0.57516028  0.19937893\n",
      "   0.09613527]\n",
      " [ 0.98763684  0.99869415  0.98731649  0.99243089  1.          0.96942619\n",
      "   0.9405195   0.9900781   0.98824735  0.98048889  0.98896583  0.97457161\n",
      "   0.59500014  0.59434842  0.77684196  0.82554098  0.57827033  0.2124312\n",
      "   0.10556222]\n",
      " [ 0.98666996  0.96660397  0.99291278  0.9871542   0.96942619  1.          0.99458\n",
      "   0.98596315  0.97570878  0.9682893   0.97226249  0.97135462  0.55810301\n",
      "   0.63193854  0.8569728   0.82209401  0.5334624   0.1813442   0.09145523]\n",
      " [ 0.96956123  0.93722995  0.97645139  0.9663628   0.9405195   0.99458     1.\n",
      "   0.97022183  0.95807501  0.95248175  0.95314881  0.95988615  0.52550371\n",
      "   0.65722404  0.85806641  0.7869162   0.4927984   0.15964375  0.07934571]\n",
      " [ 0.98937819  0.98705945  0.98881239  0.9886831   0.9900781   0.98596315\n",
      "   0.97022183  1.          0.99767415  0.99424953  0.99693114  0.99332536\n",
      "   0.55037388  0.66278235  0.77620869  0.78089622  0.51796057  0.16783195\n",
      "   0.07114918]\n",
      " [ 0.98397672  0.98567604  0.97955084  0.98070325  0.98824735  0.97570878\n",
      "   0.95807501  0.99767415  1.          0.99871327  0.99953036  0.99649827\n",
      "   0.55598678  0.69321709  0.74344517  0.75535451  0.52411368  0.18608845\n",
      "   0.0902311 ]\n",
      " [ 0.97549993  0.9773781   0.9692281   0.97001318  0.98048889  0.9682893\n",
      "   0.95248175  0.99424953  0.99871327  1.          0.99851839  0.9985407\n",
      "   0.53624129  0.72074121  0.71658143  0.72343718  0.49951382  0.17344342\n",
      "   0.08049445]\n",
      " [ 0.98193134  0.98615298  0.9777914   0.97960674  0.98896583  0.97226249\n",
      "   0.95314881  0.99693114  0.99953036  0.99851839  1.          0.99630339\n",
      "   0.54313208  0.68489265  0.73641725  0.75273373  0.51126881  0.17058245\n",
      "   0.0732393 ]\n",
      " [ 0.97475611  0.97166879  0.96862428  0.96765948  0.97457161  0.97135462\n",
      "   0.95988615  0.99332536  0.99649827  0.9985407   0.99630339  1.\n",
      "   0.51644251  0.73052675  0.71914072  0.71042852  0.47512035  0.15536289\n",
      "   0.06522393]\n",
      " [ 0.59606087  0.60617541  0.58026883  0.58762326  0.59500014  0.55810301\n",
      "   0.52550371  0.55037388  0.55598678  0.53624129  0.54313208  0.51644251\n",
      "   1.          0.42477294  0.55453249  0.6354223   0.96397975  0.86467382\n",
      "   0.80562821]\n",
      " [ 0.62177022  0.59603924  0.58172259  0.57060561  0.59434842  0.63193854\n",
      "   0.65722404  0.66278235  0.69321709  0.72074121  0.68489265  0.73052675\n",
      "   0.42477294  1.          0.26442061  0.16213301  0.34415033  0.34690232\n",
      "   0.33592886]\n",
      " [ 0.81379834  0.77196983  0.84909357  0.83718887  0.77684196  0.8569728\n",
      "   0.85806641  0.77620869  0.74344517  0.71658143  0.73641725  0.71914072\n",
      "   0.55453249  0.26442061  1.          0.93062555  0.57412975  0.20747905\n",
      "   0.12900938]\n",
      " [ 0.82660696  0.82337942  0.85200436  0.85545685  0.82554098  0.82209401\n",
      "   0.7869162   0.78089622  0.75535451  0.72343718  0.75273373  0.71042852\n",
      "   0.6354223   0.16213301  0.93062555  1.          0.68442141  0.27712308\n",
      "   0.17156707]\n",
      " [ 0.57945703  0.59244522  0.56497923  0.57516028  0.57827033  0.5334624\n",
      "   0.4927984   0.51796057  0.52411368  0.49951382  0.51126881  0.47512035\n",
      "   0.96397975  0.34415033  0.57412975  0.68442141  1.          0.87500997\n",
      "   0.8117444 ]\n",
      " [ 0.22599514  0.23151363  0.19254855  0.19937893  0.2124312   0.1813442\n",
      "   0.15964375  0.16783195  0.18608845  0.17344342  0.17058245  0.15536289\n",
      "   0.86467382  0.34690232  0.20747905  0.27712308  0.87500997  1.\n",
      "   0.99136022]\n",
      " [ 0.12745831  0.12458994  0.09252925  0.09613527  0.10556222  0.09145523\n",
      "   0.07934571  0.07114918  0.0902311   0.08049445  0.0732393   0.06522393\n",
      "   0.80562821  0.33592886  0.12900938  0.17156707  0.8117444   0.99136022\n",
      "   1.        ]]\n"
     ]
    },
    {
     "data": {
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vIecxmS2kZGQMF14xq1dKRiae2NruSkGI0V0bxzNSSkJ+P35v3yFCye/1EvD1xdZKNAV8Xvx9fYSDgTHrNJnNGNHJCwYAi9WmRFdcjNlGiLGhtDUmzuLCzGbHmlBemM0TFj+RYGCEGBrMD07uegowLIKIRRIxGQTNESKmIfETMUsiHmOEEFJiCIt56PPa7djsTux2J3anC8eg2HF5cNlTSLEOLS6LC5fVpdJWFykWlW81W4/oHhzCoFjqqh1FLLWpPF+rSgcO/R8DwJ4G7hxIyU1Om5LEYYWVEOIh4DwgWwjRAHwHsAJIKX8JPA1cAhwABoCbj1VjNRqNZrL0dbRRvW0r1du3UrfrbcIBP2aLheJFS1l2wTrKl68is7D4EIFjMplxZyoL1HhEI2H6u7vxjRBeg4Ksq6mBut1vE+zvP+RYi80+3PqVOdz9aLHbEcKk2iYEIraMm0YgTAJGrFW+sggkrkeWFwgwJdYDwf5+/F4vfl9fTCwlCKIxhNJ4IsiR4sbh8eB0p5KSnkF2cSkOTypOt0etPak4PR4cbg9OTyoOjwerzT5k6QkNd2/F8wZdXHE3V0K5UCghHTsmGCTg8w0/JraeLMJkwupwYLbZEDYLwmpGWkxELRBxSsJuSdBkJmCy4ReSfgL4pJ9+4SdsHm4lilgMwmYJVhNuVxqpzjTSHOmk29WSbU8j1ZY6TPTERZBlSAy5rC6spiQJoYkwrmXpCMRS3iJwn6/S7hxw5w2lU3LB6pi6zzYJhOrMN/WsXr1abtmyZVrOrdFoTlyikTCNe/dQvX0L1du20NlQB0BqTi6zlq9m1opVlCxehs3hnNJ2hYMBZfnq6hoSYd1d9Her7f7uLrxdnUf0UJ8uzBZLgiBSQkmtPfF8Z2oqDveQUHK43ZhMMztOTUpJJBSiy9dBh7eFjr52urztdPs66Qv04JUDeOUAPYaXHqOP7kgfXeEeQjI0Zp1uq5s0e1pcHI2ZdgylXRbX1Fo0pYSwH4JeCPZBoA+CvWo70JeQ54VgL8aAF+HvRPRPQiyNJpDceTNeLAEIIbZKKVcfrlwyXIEajUYzrXg7O+JCqnanskqZzBaKFy5myfkXMWv5ajKLDrVKTSVWu4OM/EIy8gvHLDPoQvN1deLr7iQaDiOlREoJUqqYLwlSGsjYmmH7Y2lUHI9EIg0JI9aj5xvI2HEkrqXE7nINWZISrUh2x3HnyhwID9Dh76DD30FnoHMo7e+k0985LD9shA853iIspNpTlfhxpVNgL2WhYxyhZE8jzZ527C1HhgEh7ygiaEQ6LozGEE5GZNzThAwHtdGzqAqeQe3AEiSCbHcP2Zl+sksgu8hOVnE6lvSpE0syHEZGIpicU/uyNBZaWGk0muOOaCRCU+U7KlZq+1Y66moA8GTnsPDsc5m1fDWlS5Zhc7qmt6GTRAiB3eXCZLcislOwmWykWE/MOKxkEogE6AwMCaNBcRQXSgmCyR/xH3K8SZjIdGSS7cwmy5FFRXpFPJ3tzFZpp0qn2lKn735ICQ1bYMcj0LJjuEgKehmjQ/4QwgT2VHCkqrU9FVKLEvI8Cek0te1IxR9xU11lompPgPp9XoyIxOmxMvfMHCxWEx0NPvbVe9lVE4U3QZgEGfkRsou9SmyVuMkp9uBwH524lFISaWkhuG8fgX37CO7bT3DfPkJVVeR++Utk3nDDUdWfLLSw0mg0xwXerg6qt22lZvtWanduJ+QfiFmlFnHOhz7MrOWryCounREiJBgN4g15D1n6Qn3xtC/sG7aduASiQ0HcFpNlTEtIhj1jKC/BapJmS8M8w91toxE1ogxEBugP9+ML+eiP9NMf6scX9uEL+w6xKA1ambxh76j1pdvT46JoWc6y4SLJodZZziwy7Bkz+3p1VcOOPylB1XUQLA4oXgNZs8GRNkIQJYqktAQR5QFbCkzw/8PbFaBqezvV29tp2t+BlODJdLD03GIqlueQPzsNk2moLmlI+joDdDR46aj30VHvpWl/D/veaI2XcWfYyS7xkF3sJqfEQ3aJG0/W6FbPqNdLcL8STolCyujri5ex5OdjnzcX99qzcSxdehQXOLnoGCuNRjMjiUYiNO/bS9X2LdRs20J7zCrlzsqmYvlqylesomzJKUm3SkkphwmjQ8RP+FAhNLJcyBg71gaG3EkemweP1YPb5sZj85Bqi+XZPKRYUwhHw3QHu+kN9tIT7KEn2DMsHRnDbSMQeGyeQwVZQgB0okgb3Gc324/oevkjfnxhH/3h/vhyyHbIN2zfQHhgWBlf2DeqNWkkHqsnLojiQilmWYrnO7LJdGZObeB2svF3w+6/KTFV95rKK18Ly94Pi65QgirJdLf0U7W9napt7bTVKrGaWZhCxfIcKpbnkF3invSLi98boqPBR0e9j/Z6Lx0NPnpa+hmUHjanmcwsM+mWfjz+Zlwte7Du34rR1Bivw+R2Y583D/u8udjnzcMxbx72uXMxpyX/GozHRGOstLDSaDTTxuBDuTvYTU+gh46OJjrf2Uf3Owfo21eLEQyBSWAvy8M2Jx/rnDyMLBcRI0JERghHw4SNMBEjQtgYkY6GD19mZPlYWh7GpWI1WeMiaHA9KI7ieVZPfHvk4jAffWySlJKByADdgeHCa6T46gkMzxuIDIxZp9PiHLJ6xdaptlRC0RADkYFDxFF/uJ+ByADGiPG+RsMiLLht7nh3frfVjcvqwm0dnhfv8m9LIcWSEj/GbXWT5cw6IvF33BAJwYHnYcfDUPkMREOQPU+JqWXXQHpyZyyRUtJe56VqWztV29vpblHfjdzyVGavUGIqPS85Ly5SSiLNzQT27aN/7wE69jbT0eynJ+DE6yrA5y7CiN1bgUGaM0xWvp28ebnkLi4iu8SD3Tm6ky0cNTALMcyCdizQwmqGYBhR/H19uNLSZ4SLQnNyIA2DaDSKEY1gRNQ6Go0gowbRhDwjGiUaiRySjkbUYgyuo1GMSFjVGYkQjYSHysfLRAiFQ2rairCfYChAKBwkHA4SiYSIhMNEY3XISBRpRCEqEQaYDDBJgTWqhgHot0dozPXTkOOnOStA2Dr675TVZMVismA1WYenzdax9w2mY2VG2+ewOEi1pQ6zILlt7vj28fxwD0VDhwixuPAKHCrM+kJ92Mw2NY6RLeUQITSeOHJb3UogWVW8mP4NHAUpofEtJaZ2/gX8XeDKhqXvU4KqcMWE3XcTwTAkzQd6lGVqezu+riDCJCicm0bF8lwqlmfjzji6YPNoX1+C+y4WC7V/P4Z3yGVrKSzAMXdezBI1D+ucuQTceXS2BOlo8NIecyf6vUMdCFKzHWSXeMgpcZNd7CGrOIXNTT3cubGSWy+Yw3tWFB9Vuw+H7hU4Q3j90Yd57S8PYU9JIbesgpzyCnLLK8gpm0VWcQlmy3FsqtYcFX3tbby18QmC/f1xYWNERwqeIXGkhFF0SDANipxEAWVEMSLRQ0YMPxZIE0ghMEwSQ6iBC6NCpQ2TxDAxLG0ymzHbrFgsVixWFzarDZvVgc3mwGFz4rA6SUnPIHfxQtKLi7CZbaMKJouwxNf6QT15bGYbOa4cclw5092Uk5vu2ljc1MPQeQDMdlhwCSy7FuZcAMkaiBOIhg3q93ZRvb2d6h0d+L1hzBYTJYsyOfWyWZQvy8bptk26XhkKEayuHoqDqqwkuG8/kZaWeBmTx4N93jxSL7tUufDmz1duPI/nkPpcQGYJzF2Tp+qXkoG+EO113rg7saNeWdgGGRCStQ6BrTkIKyZ/bY4FWlgdQ8LBANuefYqCufPJKZtFe001O17YGB+nxmS2kFVcEhdaOWUV5JTPwuk+9AunOT6IGlH6I/0MhAeGxZcMhAdUIG64H1/Qi++1vUQ3HQBDIp0WMAmkCTCJWFqtMSekTQIsAmzD86TZCibbocfGjj8kb0T9mAQRDPqNAfoiXvoiPnoiffiiPqKJ4kgQE0kSpy2FdEc6GfaMsdf2dDIcGXG3ksWkf2404xONGoQDUUKBCOFAlHBwKG0YkrzyVFKzZ0aX+iMi0DsUN1X7L5VXdhac9VlY9O6kxk2FAhHqdndRta2Nml2dhANRrA4z5UuyqFiRS+niTGyOI/ufjHR00P3gg3Q/+BDRnh6VabVir6jAtWYN9nlzlYiaNw9Lfv4RvwAJIUhJs5Oy1E750mwAmnr8/M9Te3l9WwvlZivn5qZRGpDMzpg5PYD1L90xZM+rLxHw9nHOF79B8cIlgHIN9rQ001ZTRXtNFe211dTs2Mbul1+IH+fJziGnbBa55RVxK1daTu6Mm+/qRGAwxicugg4jig7ZFxkYVi6xN9doZPXYOHNXFll9NprzQ+xdaRBKmdiPzmTd9oeLE0rELMzxIOZSRxmnHEYo2cyTf7vVnHgYhiQcjBIORAgFokoUBWOiaDAvQRyFYmWVeFL7wsGhY6ORw1taU7MdFM3PoHh+BkXzM0hJm+Fu2WgYDrwwFDcVCUDWXHjXt2DpNZBRlrRTBXxhqnd0ULW9nfp3uohGDBxuK3NX5TJreQ4lCzIxW4/8ORI8cIDO+++n7/EnkOEw7vPPJ3XDBhwL5mMrL0dYj50HxheM8MuXDvKbTVVI4MMXzOKT588m1THzvD5aWB0jpJS89fTj5M6aTdGCxfF8k8lMZmExmYXFLDjznHh+f0837bXVSnDVVtNeW031W1viLh2b0xm3ag1auLJLytRs35oJIaWksruSZ6qf4cW6F2n3tzMQHpiwAHFanMPm0UqxppDnyotPJTE4l1Z8O6GsLWKm+qkXOPDay6Skp/OuL3yCuaeeqV1ZmilDGpJI2IiLGSWIBsXNKEtgqFwotj1SIEVCE3M5CwFWhwWbw4zVbo6nnR4rNocFq8Mc2zc8bXMMlVWxQb00VnZTta2dPf9SU9Jm5LuUyFqQQdHcjKMeKykpSAlN25RlaudfYKADXFmw8gY45VooXJm0uClfd4Cq7UpMNe3vQRoSd6adxecUMntFDvmz048qqFtKycDrr9N53330v7IJYbeTdtV7yLzxRuyzZiXlM4xHJGrwpy0N/OS5fXT4glx+SiFfWTefksyZY6EaiQ5eP0bU7NjGo3fcxoZPfYFF57zriOoIh4J01tXSVltNe20VbTVKcIUDqkuyMJnILCxWQismtnLLK3ClTm0X1JnOwZ6DbKzZyMbqjdT01WAWZk4vOJ1ZabOGiaJDBFHCxKMuq2vsmdcPw/43X+PF3/4SX3cXyy++lLOvvQG7a+b+KGhmBqFAhOBAJC50IqMKoLEFUmjEdiQUPez4kYMIgRJAdjMWu1mJH7s5LnSsDjO2BIFkHSxziEBSa4vVlNSXCMOQdDb4aNjbTUNlN00HeogEoyAgu9gdt2gVzknHNkZPsmNCTz3s/BO8/TB07FNxU/M3KDE158KkxE2FAhG6WwaUwNzeTmu1GtcpI9+lhkVYkUNOqefoe52GQvQ98wyd9/+O4J49mLOyyLjug2R84ANYMjKO+nNMhJf3tfP9p/ZQ2eplVVkG37p0IStKp+bco6F7BU4zj915O23VB/no3b/FkkTzqDQMetpalFWrpoq2miraaqvxdXbEy7gzMocJrZyyCtLz82f8/FzJpL6vno01G3mm5hn2d+9HIFiTv4b1s9ZzYemFZDiO/T9nX0c7L973Kw5ueZ2csllcdMunKZgz/5ifV3N8EPRH8HYG8Hb66esM4O0KxLbVEug/dDqV0TBbTHERZHWYsdjMQ9uxvMRtWzxtGXW/1W7GnGQhdKyJRg3aarw0VnbRUNlNy8E+ohEDYRLklnniFq2CijQstiT/Dgb64J2/K+tUzSaVV3omnPJ+WHQlONOPqNpBAdXV1E9Xcz/dzf10NfXj7RoKN8gt81ARGxYhIz8lGZ+GaG8v3X/6E91/eIBIWxu2ObPJuukmUi+/HJN9atyulS1e7nh6D6/sa6c008XXNixgw5Ijj9VKFlpYTSNdTQ3c9/lPcOY113HGez8wJef0e/uGXIkxsdXVWB+fYd5it1Mwex4VK9dQseo0MguLpqRdU0lLfwvP1jzLM9XPsLtzNwArclewrnwdF5ddPGW9oAwjyvaNT/LqIw8gDYMzr/4gKy95N2aL9ryfLEgpCQ5EhoRSV4C+Tn+gOIZEAAAgAElEQVQ87e0MEBwYPrinxWrCk+WILU48mXacbtu4IsliN2M269jLkURCUVqqemmo7KaxspvWGi/SkJgsgoKKtLhFK7c8FbPlCK5fNAIHX1RxU3ufUnFTmbOVZWrZNZBRPuGqQoEI3c0DdDX76GpWQqq7ebiAMltMpOe7yCxIiS85ZR48mcmbgy/U0EDX735Pz6OPIgcGcJ1xOlk330zK2rVTJmjavAH+33P7eOTNetx2C7deMJfrzyjDbpkZRgEtrKaR5//vF+z65z+45Z77cKUd2dtKMoiEw3Q21MUtW/Xv7IzPqZZRUETFqlOZvepUiuYvwmSeGV/cydLh7+AfNf9gY81GtrVtA2Bx1mLWl69nXfk6CtwFU9qe1qoDPPebu2mtOsCs5au44COfJC03b0rboDn2SCkJ9IfjwqkvQUANWqDCgeiwYyx2M6kx4ZSa6cCd5SA1y6mEVKYDp8c67W/kJyqhQITmA0NCq73eC1Ldk8I5aRTNy6B4QQbZJZ6x45EMAxq3wu7HVNxUfxs4M2DJ+5SgKlo1btxUKBAZZnnqiokpX1cwXuYQAVWo1qnZDkzHSED7t2+n87778T73HJhMpF16CZk33YRj4cJjcr5R2xCK8n+vVvGLlw4SjBhcf0YZt75rLhkpMyuGWAuraSLg8/GrT97IgjPPYd0nPjvdzTmE3rZWqt56g4Nb36B+906MaARHipvy5auYvepUypevwpHinu5mjktPoIfn6p7j2epnebP1TQxpMDdjLuvL17O+fD2lqckdnXgihAJ+Nv/pj7z19OO40tI4/6ZbmHf62fpBeZwipcTvDcdE03BL06DbLhIcLpxsDrOyNA2Kp0HrU6YSUPYUPe7WTCHQH6ZpXw8NlSpGq7u5HwCb00LRvPS4RSsz14qo3aSsUnufBl8LmG0wb30sbuoisAx/+A8KqEHLU1dsGSmgMgpcZOQPiafMghRSc5zHfPRwABmN4n3hBbruux//tm2YUlPJeP81ZHzoQ1jzpu5F0DAkf9veyI+fraS5N8C6xXl8bcNCZmUnx62ZbLSwmibe+Ptf2PTg/dzwo5+RU3bse0wcDSH/ADU7tlG19Q2q3noTv7cPk9lM0YLFzF51KhWrTiUjv3C6mwmAN+TlxboX2VizkdebXiciI5SllsXF1JyMOdPWtoNb3+CF3/4Cb0c7p1y0gbM/cOOMF6cnEom93SKhhIDt0PCA70jIiOfHg8FHlAmHDCLBKH5viEh4eI83u8syTCgNue2UiLK7ZkBvNM0R0d8bpHFfN417u2nY20VfpxJBTlMvRbYdFDv3UTQ/g7QV5yLmXQTOdEL+SFw0JVqifN0JAspqImPQAlWYEhdSqdlTI6BGYgwM0PPYX+n6/e8J19VhLS4m84YbSH/vVZhSplbMvF7VyR1P7WFnYy9Li9L41qULOa0ia0rbMFm0sJoGopEI9976UTILCrn6tu9Pd3MmhWFEad6/j6qt/+bg1jfobKgDILOwOO4yLJy3cEpdhgPhAV5ueJmN1RvZ1LiJsBGmMKWQdbPWsaF8AwsyF0yrBcDX1cmL9/+K/f/eTFZxKRfd8hmK5k+d+fxEQhqSlqpe2uu9w3uyxcROYs+2kQJqol3+BzGZxLAgb4stIfjbpuKWHG5rzOLkVOtMx9T2LtNMLb52qHxaWaaqXqIvlEojZ9BofRcNvaX096vfGXeGnfQ8Fz2tA6MLqATrU0bB9AmokYTb2uj+44N0P/wwRm8vzlNOIfPmm/FcdCFiisNAqtp93PnMXv7xTisFaQ6+sn4+7z6laEZcp8OhhdU0UPnaJp6864dc+ZXbmL3qtOluzlHR29bCwa3KZdjwzi7lMnR7mLVitXIZnrISuyv5bzjBaJBXG15lY81GXm54GX/ET44zh3Xl61g/az3LspdNuzvFMKK8/dwzvPrQ7zAiUU5/77Wsvvw9enqiSSKlpK3Gy/6trRzc2jbsQSVMAqvNhCUmdqyOIdGj1ibVqy2xzGBAdzxtGtZDbjB9RMHKmhOPruqYi+9JqHsdkGqS4wWXw8LLoOQ0MJmRUtLb5lduw73deDv9h8RBebJmhoAaSaByH1333UfvU09BJILnwgvJvPlmXCunfu6X7v4QP31hPw+8XovdYuKT58/hI2fPwmE9fuJ7tbCaBh687Uv4e3v58F2/OqFGSQ8ODFDz9ltUbf03Vdu3Eoi5DIsXLom5DE8jPS//iOsPR8O81vwaG6s38mL9i/SH+8mwZ3Bx+cWsK1/HytyVmGfIUBFtNVU8/5t7aD5QSdmyFVz4kU+Snj+1AfLHM1JKOup9HNjayoGtbfR1BDCZBaWLs5izKpeShZnYnRZMFjHtAlpzgiEltOxUQmrvU9C6S+XnLVVCasGlkLckqRMeTwdSSvpf/Rdd991H/+bNCKeT9KuuIvPGG7CVTn38aTAS5feba/nZi/vxBSO8f00pX7hoHjmeGT5i/ihoYTXFNO+v5MFvfZHzb/o4KzdcPt3NOWYYRpTmfZUcfOsNqhJchlnFpVSsOpWKlWsonLfgsGNmRY0ob7a+ycbqjTxf9zy9wV48Ng8Xll7I+vL1nFpw6oyaWy4cCLD5Lw+y9am/4XB7OP/Gj7HgrHP1w3+CdDb6OLC1jf1bWult82MyCYoXZjBnVR4Vy7N1fJLm2BCNQP3rQ5apnjoQJig9QwmpBZdOamiEmYwRCtH35FN03X8/wX37sOTkkPGhD5Hx/mswp09973QpJU/vbOGHG/dS1zXAufNy+MYlC5mff/zOhTtRYTVznlzHOW898zg2p4sl510wLP/t9rf53e7fETWiCCHio3ebhAkx+CfUYkINyhfPY5TyCfsPyRssLxizLoEYc39iG8Y6Z/wcq1ykrD4fR5cPf2UDA3vrefOJR3nz73/B5LKTMr8Uz8JyUuaWYnU64udFwtbWrTxX+xydgU5cFhfnl57PhvINnFl4JtYkzuieLKq3b+X5e39OX3srS991MWuvu1lPlD0Bulv6Y2Kqje7mfoSAovkZrLiolNkrcmfG1COaE4+wH6pegj1Pwr5nYKBTjYA++3w45ytqJPSU7OluZdKI9vTQ/fAjdP3xAaLtHdjnzaPgBz8g9dJLME3TlGfb6rr53lN72FrbzYJ8D7//8KmcM29qxhGcCWhhlQS8nR3se/1VVqy/HJtzaKqSDn8Ht754K4Y0yHHlIKVUCxIjNgfgYHowP3H/yG1QbwEGxrC6EvePV9cxZQFYZwuKOpyUtDop2jWAd9t+okLSmhmgPs9Pfa4fnyuC3WznnOJzWF++nrXFa3FaZuZs9f093fzz/l9T+domMguLef937qR40ZLpbtaMprfdz4Gtrezf0kZngw8EFM5JZ+m185i9MhdX6swal0ZzguDvhn3/UFapAy9AuB/saTBvnbJKzbkQ7CdWT91Qba0a0POvf0X6/aScfTaZd95EypnTNwdpfdcAP3q2kifebiLHY+fOq5Zy9eoSzDMw/uxYooVVEtj+j6eQhmTF+svieYY0+MambzAQHuChSx+a1uEABhkpuEYKs0G3cFzUDZYZRaCNJuDiAk9CNBqh4+BBmt7eSdqOXRS+08pp74CnMJ+ShUvJGygnsyeLcIoXR5Z9RsWkScNgxwvPsunB+4mEgpx5zXWsueJ9SZ2aKByKcmBLGzU7OkhJt5NZmEJWoQqEPd7cYt6uAAe2tHFgaytttV4A8malcvbVc5m9Mhd3xvEXS6E5DuhrGnLx1bwKRgTc+Wp8qQWXQvnaQ8aYOhEwgkFa7/g+PX/+M8JiIfXyy8m86UYc8+ZNW5v6AmHu+ecB7vtXDSYBt75rDh8/dzYp9pNTYpycnzqJhIMBdjy/kTlrTictdyiA+7e7fstrza9x+xm3zwhRBcRdeUzRy0P56lmw+kIAulua4uNlVb3+Gu/0PxcvZ7HbySwoJrOomMzCoXV6QSFW29Q+lDvqanjuN/fQtG8PJYuXceFHP5XU6X+6mvvZvamRytdbCA5EcGfYCe6NDBul252hhFZmoTsutjIKUrAme46zo6C/J8iBt9o4sKWNlqpeAHJKPZxx1WzmrMolNWtmWiE1xznt+2DvE0pQNW5VeVlz4IxPw8LLoXAlzKCXtGQTbm2l4TO3Etixg8wbbyDrox/FkjN9LrZw1OChN+q46/n9dA+EuGpFMV9aN4+CtJP7/18Lq6Nkz6aXCPi8rLzkinje9rbt3L3tbtaXr+equVdNY+tmDhn5hay69EpWXXqlGtW6r5euxga6mhroaqqnq7GB5v172bv5FdV7B0AI0nJyE8RWSTztTE1Lqrk7HAry78ce4c3HH8XmSmH9Jz/PonPelZRzRCMGVdvb2f1KI437ejCZBbNX5rLknEIK5qigUl93kM5Gn5rqoqmfziYfjZUNRCOxMZoEpGU7lWWryB0TXimk57mmbK64gb4QVdtUzFTTgR6QkFXk5rR3VzBnVS7pua7DVzIevjbwNkNaiZoqRHcM0AAEvfDWH2DrfdCxT+UVroQLvg0LLoOck2Ni84G33qLh1s8iBwYovvtneC68cFraIaXk7YZent7ZzFM7mmns8XN6RSbfunQRS4rSpqVNMw3dK/AokFJy/xc/icVm40M/uAshBL3BXq5+4mrMwsyfLv8THpsOcp4M4VCQnuYmOhvrE4RXA91NjURCQ+McOVLcZBQVk1VUMszKlZabP+lBTGt2bOP5e++ht7WFxedewDkf+jCu1KP/gejr8LN7UxN7Njfh94ZJzXaweG0RC84omFCskRE16OsI0NmkBFdnYz9dTT562vxIQ/3fmsyC9DyXsmwVDVq43KRmORBJiGsI9Iep2tbO/i2tNFZ2IyVk5LuYszqPuatzycg/irHMAn1Q+y+oehmqX4a2d4b22VPVmELppZBelpAuhYwycOgf8BMebwv8+5ew5bcQ6FXjSi29GuZfAmkn3iTy49H9yJ9o+d73sBYWUHL33djnzp3S848mpqxmwdlzsrnutDIuWJh7UvSQ1r0Cp4DaHdvoaqxnw6e+gBACKSXf2fwd2v3t/GHDH7SoOgKsNjs5ZbMOmQ5IGgbezg66GuvjYqursYHqbVvY9c8ht6LJbCGjoHCY2MosLCajsBi7a7hFZaC3h5d+fy97Xn2JjNho+aVLlh1V+42oQc3OTnZvaqTunS4EUL4smyXnFFGyMHNSYsdkNpGe5yI9z8XshPH8omGD7tZBoaXEVkt1H/u3tMXLWGwmNXhh0ZA7MavQjSvNdtgfwKA/QvX2dvZvaaNhTxeGIUnLcbJyfRlzV+eRWZhyZD+ikSDUv6FEVNXLypUjo2BxQOnp6qGZWQF9jdBdq7rGd9dC9SsQ8g2vy5GWILrKhouu9FKw6/+945a2vbD5Z7DzTxANKxffmbdCyZrpbtmUI0MhWu74Pj2PPELK2rUU/fePMadNzUvFeGLqcxfO5eJF+aQdZ/GgU4UWVkfBW0//nZT0DOadsRaAhysf5oW6F/jS6i+xJFv3HksmwmQiNSeX1JxcypevGrYv4PMNia2Y4OpoqOPAlteRxtB0J+6MTDKLiskoLMHpSWX7xicIBQKc/t5rOe3Ka7AcRddkX3eQPZubeOfVJnzdQVLS7ay5dBaLzirAneE44npHw2w1kV3sIbt4uHhInPy1q1G5E+t2dbJ3c3O8jN1liYss5VZUFi6TWVCzo4P9W9qoe6cTIyLxZDo45cIS5q7OI7vEPXkxZUSh+e0hIVX3OkT8ahyhwpVw9ueh4lwoPhWs41wjKVWvr54EsdVTp5bOA3DwRQgPDD/GmTHC0lU2JLrSS8E2Myd5PWmRUgWgb/4Z7H8WLE5YeQOc8Skltk9CIu3tNHz2c/jfeousj32MnM999phPP6PFVHLQrsAjpLOxnvu/8B+cec11nPHeD7C3ay8ffOqDnF5wOndfcHd8/CnN9BGNhOlpbVFiq2G4pSvkH6BowWIu+tinySouOaL6pSGp39vF7leaqN7RgTQkpYsyWXxOEeVLszBNUezT4fD7QkOxW7E4rs6mfkL+SLyMMAmkIUlJtzNnVS5zVueSV546OTElpRI6VS+ppeZVCPSofTkLlYiadS6Un5VcV56Uaqyi7toh8dWTIL566iASGH6MK/tQK1eiELM6wTAgGlSWtmhoxDoIkdCI9WHKRcOTPDYM+Utg8VUwf/2JaYWLRmDP35Wgatqm7supt8Caj0LKzJ6Q91ji37GDhk9/hqjXS+H37yB1w4Zjdq7xxNQlSwu0mEpAj7x+jHn+3p+z66XnuOWe+8Bl4/1Pvp+B8AB/vuLPZDoyp7t5mnGQUhLo9+FIOQIrDOD3htizuZndmxrp6wjg9FhZeGYBi84uIi3n+OgNI6WkvydIZ8y6FegPU7Yki4LZaZOLzeprGoqRqnoZvE0qP61EiaiK82DWOeDJOxYfY2JIqQLj44IrQXR110JvvRIziZgsqvt+sjBZwWIHs23E2q6GBBhtLUxKnHqblLt07sWw5CqYuw5sR9lRYLoJ+mDbA/D6Peo+ZM6GMz8Np3xAidqTmJ5HH6Pl9tux5OZSfM/dOBYsSPo5tJg6MnSM1THE7/Oy+5UXWHj2ebjS0vnmq9+kzlvHvRffq0XVcYAQYtIjp0spaT7Qy65XGjm4rQ0jIimcm87p755NxfIczNaZYZ2aKEII3BkO3BkOyhZPwjLg71YP+6qXlJDq3K/ynZlKQA1apTIrZk6vPiGUsPPkjR6nYxjgax0SXt21aoDJYWLHPo4QOoxgMtuOfAgAw4D6f8Pux2D332DP42BNURasxVepgS/Hc6PONLyt8Mav4c17lTWz5DRY9wMVkH4CD5MwEWQ4TOsPf0T3Aw/gOuN0in7yEywZGcmrX7v5pgwtrI6AnS88SyQYZOWGK3ji4BM8fvBx/uOU/2BN/skXXHmiExwIU/nvFna90kR3cz82p4Ula4tYvLaIzMKTIE4nNKDmWhu0SjW/DdIAqwvKzlRxMBXnqclrj9cHo8kEqQVqKT1tulszHJMJys5Qy/o7VS/KXY8pgbXrUdV7cv4lypJVcf7MHRCzvRJeuxvefjgWkH4ZnPGZmXe9p4lIVxeNn/0cA2++SebNN5P7xS8gLEf/eB4UU0/taOLpnS3DxNTnL5rHRQvztJg6BmhX4CSJRiLce+tHySwoZM2tt3DNk9ewKGsR915874yaNFhzdLTW9LH7lUb2v9lKJGyQW57KknMKmbM6b0YN1Jl0ohFoemtISNX/W7nJTBYoXhNz750LRatn7kP8ZCAaUfdn92Ow5wk1HIEjXQmWxVep+2Se5t8jKaF2s4qf2veMcmcu/6AazDNr9vS2bQbh372bhs98hmhnFwX/9Z+kXXHF4Q8ah/HE1KXLCrWYOgq0K/AYceDN1/B1dnDuTR/ly698GbvZzp1r79Si6gQgHIyy/81Wdr3SSHudF4vdzLzT81mytoic0hMwcHiQoBe2/VG592r/BcE+lZ+3VAUSV5wHpWeccHOtHdeYLTDnArVc+v+g6p/KkrX77yp2yZUFC69Qlqyys8A0hS8DRlRZ1Db/TA2p4cyEc78Gp37shJr8OBn0PvEEzd+6DXNmJmV//CPOJYuPqJ6xxNTauTnaMjUNaDUwSbY+/XfS8wr4q/EKe7v2cve77iY/Jf/wB2pmLJ2NPna/0kjlv1sIBaJkFaVwzrXzmHdaPnbnCf4v4muHP75XufgyZqkH8axzVbyUfggeH1hsarLheesgHIADzytL1o5H1Gjl7jxY9G5lySo57di5bEP9sP1B5fLrrlFxdpf+D5zyweM/2D7JyEiEtv/5CV333Ydr9WqKfnoXlqzJ9YI8rJhalEeaU4up6eAEf2okl+b9lTTv20vRu8/jwcrfcf2i6zm35NzpbpbmCIiEoxx8S00z03ywF7PFxJxVuSw+p4j8ikkOM3C80lMHf3gP9DbCBx5RAdGa4xurQ7kDF16m4uP2P6ssWW/9XgWNpxbBoiuVgC5alZwOBr42eOM38OZvVOeG4jVw0X+piZCn0lJ2nBDp7qbpi1+kf/NrZFx3HXlf+ypiEhO8h6MG//dqNX94rVaLqRmKFlaT4K1nHsfqdPKLyF9ZlLWIz6383HQ3STMJ/L4Qdbu7qN3ZQd07XQQHIqTlOjnrfXNYcHoBDvdJ9IPUtleJqnA/3PA3NfK55sTC5oLF71FL0AuVG5Ul683fqGEO0ktj+6+CglMmL7I69ivr1PaHVBzegkvhzM/o79I4BCorafjUp4m0tlJwx/dIf+97J3X8trpuvv7YTva2eIcC0LWYmnFoYTVBvJ0d7Hv9VVoWWAlaovz4nB9jM+vg3ZmMlJLORh81Ozup3dlBa3UfUoLTY2XWKdnMOy2f4nkZSZlT77iiYaty/5ltcNPTahBKzYmN3QPLrlaLvwf2PqVE1mv3wL9+qtx2i69SlqzcRWOLLCnVCPqbfwaVT6vv0PIPqhHSs6d2/rrjjb6NG2n6+jcwezyUPfAHnKecMuFjfcEI//1sJb97rYY8j4NfX7+KixfrEJSZihZWE2T7P57CMKK8klPHt0+/g9LU0ulukmYUwsEoDZXd1OzsoG5XJ75uNXFzTqmH1ZeUU7Y0m9xSz8knpgY5+E94+Dpw58D1fz1ppws5qXGmw4rr1DLQFRu64TF49Sew6b8he74SWIuvgpx56hgjCnufVIKq4c1YQPpXYM3H1HdJMyYyGqX9p/9L569/jXPFCop+ehfW3NwJH//8O63c9vddtPQFuOH0Mr60bj4eh7ZQzWT0cAsTIBwM8PNPXM9BTycZ167lu2d+d7qbpEmgr8OvrFK7Omis7CEaMbDazZQsyqRsSRZlS7JISbNPdzOnn3f+Do9+FLLmwvWPgUe/8WoS8LWp78juv6phEpBqfLKK85R1qqsKMsrVcAnLr9MB6RMg2tdH45e+RP8rm0i/5hryv/VNxATnJG3rC3D7E7t5emcL8/M8/OC9S1lZmrwBQzWTRw+3kETefPFJIgN+us9wc+ear053c056olGDloO91O7spGZXJ93N/QCk5TpZck4RZUuzKJyTftyNhn5M2Xo/PPl5FVj8wUfUJMUaTSLuXDUkwqkfg75meOdvypL12t0q0P3q38HCy3VA+gQJHjhAw6c+Taipifzbbyfj2vdP6DjDkDz8Zj0/eGYPwYjBl9fN52NrK7BZ9O/Z8YIWVochakR56W8P0J8W5lvv+19cVv2WNh34fSHqdikhVR8LPDeZBYVz01l0VgHlS7NJz9P35hCkhH/dBc/fDnMugmt+B7aTYMR4zdGRWgCn/4daQv1qpP2ToadskvA+/zxNX/kqwuWi7P77cK1aNaHjDrT5+MZjO3mjpovTKzL5/nuWUpGjx4873tDC6jDc+8SPsPdEKLjqPOZnzp/u5pw0SCnpaPBRG3PxtVT3gQRnqo1Zy3MoX5JFycJMbCf6OFNHg5Tw3G0qLmbp1XDlL8CsYzM0k0QL8QkjDYOOe35Oxz334Fi6lOKf/S/W/MO73IORKL946SA//+dBnDYzP3rfMq5eVXxyDPtyAqKfSuPwdvvbVD73AvmuFD70ni9Od3NOeMLBKA17u6jZ1Tks8Dy3zMOaS8opX5ZNTslJHHg+GaIReOKzsP0BFWC84UfH71x+Gs1xQNTno+krX8X34oukXXkl+d+9HZP98LGdb9Z08fXHdnKgzccVpxRy22WLyPHomNDjGS2sxqAv1MftT3yFs9sdrHrve7BOMOBQMznGCzw/9fIsShfrwPNJEw7Aox9RvbjO/Rqc9zXtxtFojiHB6moVT1VbS943v0nGh647rLWp1x/mRxv38sd/11GU7uS+m9dw/vyJ9xbUzFy0sBoFKSW3b76dnD1BTBYnp667crqbdNwjDYkhJUZU0lbTpwLPd3bQ3TIAxALPz00IPNeBmkdGoA8e/iDUbIL1P4TTPzHdLdJoTmi8L71E05e+jLBaKf3tb0k57dRxy0sp2birhe88vpsOX5CPnj2Lz180jxS7fhyfKOg7OQp/3vdnXt7/Ah9sLmfR2vNxpaVPd5OOCCNq0N0yQHu9l446H/29QSVwDImUSuxIQyKlxDCIp1WZxG0S8mPHxPIOqcuQGCO2RxvRYzDwfPHaIsqWZOnA82TQ3wEPvBdad8FVv4Fl10x3izSaExYpJZ2/+jXtP/0p9oULKPnZz7AWFY17THOvn9v+tpvn97SyqCCVe29czbLi4/P5ohkbLaxGUNlVyQ/f+CEX9y2BcC8rN1wx3U2aEOFQlM5GHx31vpiQ8tLZ2E80YgBgsZpwZzoQJoHJBMIkEEIgBLE8odZmgbCY4tuD+wfLx48dsd8kBvNGbCccP3hsep5LBZ479NcvafTUwx+uhN4GuPZBNSGv5rhASkk4KvGHowTDUfyxJRg2KM9O0dOVzECM/n6avvFNvM8+S+pll1HwX/+Jyekcs3zUkDzwei0/fraSiGHwjUsW8OGzZmExa8v8iYh+siUwEB7gy698mXRrGqUHzGQvWUZO2azpbtYhBPrDdNR7aa/3xdc9Lf1xy5DdZSG7xMPS84rILvGQU+IhPc+JSf8Tn5i0V6p5/4I+uP5vUHbGdLfohCASNQhEDPyhKIGwWvzhKIGwEVvH8kKD+4x4XiAukA7NC4QPrdMYY5xmi0lwekUWFy3K48JFeRSlj/3w1kwNobo6Gj71aYIHD5L7la+QefNN48ZT7W3p4+uP7WRbXQ9r52Zzx5VLKc3SFvoTGS2sEvjBGz+gpreG7+d8jsqux1j50U9Na3uklPT3hGLiyausUXVevF2BeJmUdDs5JW5mr8ghp8RDdokbT5ZDd9M9WWjcCg+8D0wWuPkpyF863S2a8YQiBq19AZp7AzT3+tW6J7aOLb3+EOHokc1KYbeYcFjNOK1mHNZY2mbGYTGT67EO5VnNY5Zz2sxYTIK36np47h0Vj/Odx8P/fSAAACAASURBVHezqCCVixblcdGiPBYXpur/8ymm77nnaP7WbQCU/ObXuM86a8yygXCUu188wC9fPkiq08pd71/Ou5cX6nt2EqCntInxZNWTfH3T17ll2S1kPVaLv7eXD9/1K8QUdVGXhqS33R8TUEPWKL83HC+Tluskp8RDTqkSUNnFHlypurfiSUvVS2reP1emslRlzZ7uFk07o4mmlt4ATT1+WvoCNPUE6PAFDznOY7dQkO4gP81JYZqDdJcNp9WM02bCaTVjH08IJeZZzJiOwXAgVe0+nnunlefeaWVrXTdSQlG6kwsX5nLRonxOq8jEqi3Sx4xwczMt37sD3wsvYF+4kOL//Sm2kpIxy28+2ME3/7qL6o5+rlpZxLcuXURmiv6tPt6Z6JQ2WlgBtX21XPPENSzIXMAds7/KI7d9hfNv+jgrN1x+TM4XjRh0NfcrAVWnBFRHg49wMAqowO7MwpSYG89NdomH7GK3jknSDPHO42pIhaw58KHH1EjZJziDokkJJD8tvSOsTr1KNI38SfPYLeSnOShId1KQ6qAg3UFB2v9n7z6jo6q6OIw/N5Pee2+UEEIHkSJFUFCKYkMpoij6YkEFBDtix4IgICCgIoKIoIKAIlVFEBSlJ5QkQEJ6731m7vthKAESCJBkkpn9W4sVkrm5Z4cF5J9zzt3HFj8XO/xcbPF1sW1Uh9pmFZax7Vg6W46ksSMmg9IKPU62lvQN96Z/Kx9uDvfCuRF9PQ2ZqtWSs3w56bPngF6P17PP4P7wwyhWVf/55haXM23DUVb9l0iwuz3T7mlLzzDPeq5a1BU5K7CGynXlvLD9Baw0VnzY+0P2frkMazt72vS5tVbur9PpSY8rION0wbklvezkIvRnlhksbTR4BjjSspsvnsGG/VDufg5yzp2o3t6v4ecJENAZHlxlMuf+5RaXE5NeeNHSnCFAJVcTmhxtLM+FowhfZ3xdbPGvNPPU2EJTTXg42vBA5yAe6BxESbmOnbGZbDmSyraj6aw7mIyVptK+rAgf/GVf1jUpiYwidepUSo8cwaFXL3zfmIp1YGCV16qqyrqDybzz8xFyiit4qk8znrslDDtrOVfRHNUoWCmKMgCYDWiAL1RV/eCi10OAxYAXkA2MUlU1sZZrrROf7P2Eo9lHmdN3Dg6llkT/vZOOA+7E2u76NhcW5ZVxZGcyUX8mUZRXDoCtoxVeQY60vzXo3H4oF2/7Olk6ECZq5yzY+gY07wcPLG20x42UVug4kpLPwYRcDibkciAhl7is4guucbDWGGaZXGwJ93U6N8N09mN+JhiarpadtebcniudXmX/6ZxzS4ZT10YxdW0UbQKc6R/hS/9WPkT4OckenyvQFRaRMWc2Od8sR+PhTsAnM3EaMKDaP7eE7GJeXxvJH8czaB/owtIxXWnl71zPVYuG5IpLgYqiaIBooD+QCPwLjFBV9Uila74HflZV9WtFUW4BHlVV9aHL3bchLAX+fvp3nvv9OR6MeJCXu7zMjm+X8O+61Tw2ZxEu3lc+3+liqqqSeiKPw9uTOLEvHb1OJbiVOxE9/PFp4oyjm438pyaujarClqmwaw60uQ/uXgCWjWPPhl6vcjKz6FyAOpiYy9GU/HObw72dbOgQ5EqHYFci/JwJcLXD18VWlrOuU2y6YV/W1qNp7Ku0L+tsEOvS5PL7stSKCrKXLydn6TLsOnXC88knsGnevB6/gvpXsG0bqe+8izYtDdfhw/CeOBGNc9UhSavTs2RXHDM2R6MoMPm2cEbfFIpGflA2WbW2x0pRlO7Am6qq3n7m/VcAVFV9v9I1UcDtqqomKobkkKeq6mUju7GDVWpRKkPXD8XfwZ9vBn2DotWz6KlHCGrdjiGTXr2qe1WU64j5N43DfySSmVCItZ0lEd39aHNzgDS+FNdPpzUs/e1fBjc+fubcv4a7xJCeX3ouQB1IyOVQQh4FZVrAMAvVLtCV9kGudAhyoUOQG74utkau2PRlFJTx27G0M/uyMinT6nG2taRvyzP7slp4XTD7V/T3P6S++w7lsSew69CB0uho1JISnG67Dc8nn8A2IsKIX03tq0hJIfW99yjcug2bFi3wfetN7Dt2rPb6yKQ8Xll9mMNJedzS0pt37m4jrTDMQG3usQoAEiq9nwh0veiag8B9GJYL7wGcFEXxUFU166KixgJjAYKDg2swdN3Q6rW89OdLVOgqmH7zdKw11hz87VdKiwrpNKjmDUHzMkqI3J7I0V0plBVrcfd34OaR4bTo4iMbzUXtuODcv5egzysN6ty/ojIth5PyDEHqzK/kPEM7EI2FQktfJ4Z08D8TpFxp5uUoP9EbgZeTDcNuDGbYjcEUl2vZEZPJliNp/HYsnbUHkrHWWNCtmQeDfCzo/OsyKrZuxiowkMD583Hs2wddbi7ZX39NzjfLKdi0Cce+ffF8+ins2jbu9h6qTkfO8uVkzJqNqtfjPXkS7qNHV7s5vaRcx6yt0Xyx8xRu9tbMHdmRwW39ZCVCXKAm3/2r+htz8TTXZGCuoiiPAH8CSYD2kk9S1UXAIjDMWF1VpbVowcEF7Evfx/u93ifEOQRVr2ffhrV4N2lGQMvWl/1cVa9y+mg2h/9IJD4yC0VRaNrBi3Z9A/Br7ir/wETtKSswnPt36s8Gce6fVqfneFoBBxPyzi3rxaQXnGtuGexuzw2h7owJdKFjsCut/V2wtWq4M2vmyt7akttb+3J7a190epW98TlsO5SA+v0KwvdvoFBV2d5lCJYjHuKWiGBaApZubnhPmIDHmDFkL1tG9tJlxN3/AA49e+L59FPYd+pk7C/rqpVERpH6xhuURkVdcXM6wOHEPMav3M/JjCKG3xjEKwMjcLGX5WpxqZoEq0SgcsOOQCC58gWqqiYD9wIoiuII3Keqal5tFVmb9qTsYdGhRdzV7C7uaHoHAPGH9pOdnMjAcc9XG4zKiis4uiuFyO1J5GWUYOdsTeeBobTuFYCjm019fgnCHBRlwvKhkHII7lkE7YfV6/CqqpKYU2JYzjttWNY7nJRHaYXhiCRXeys6BLkyoI0vHYJcaRfogoej/DtobDQWCq0So/CY/x7l8fEovfrw1+2j+D0NDmyPZ/r2eILc7ZjYrwX3dAxA4+yM17hxuI8eTc6KFWR/tYT4kQ9i36WLIWB17drgf7jUFRaR+ekcspd9U6PN6Tq9yoLtJ/hkSzSejjYsf7wrPZpLCwVRvZrssbLEsHn9VgwzUf8CI1VVjap0jSeQraqqXlGU9wCdqqpTL3dfY+yxyirJ4v719+Ng5cDKO1Zib2XY//Tj+2+QEXeS/81bjMbywp9AMhMLObw9keh/UtGW6/Ft6kLbvgE06+iNxlJaIog6kJcIS++GvAS4/2sIH1DnQ+YWl3MwMe/cct7BxFwyCw1Ps1pbWtDG3/nccl6HIFeC3e0b/DdQcXnliYmkffABhVu3YR0Sgs+UKTj26nnu9fSCUrYdTee7fxM4mJDLrS29mXZvW3ycz++J0xcXk7NqFdlfLkabkYFdx454Pv0UDj17Nsi/H1ezOR0MT/xNWnWQPXHZDG7nx3t3t8HVvnE8NCJqX602CFUUZRAwC0O7hcWqqr6nKMrbwH+qqq5TFGUo8D6GJcI/gXGqql7a3riS+g5WelXPuG3j2JOyh28Hf0u4ezgAWYkJLJn0FD0eGEW3+4YDht5TJ/dnELk9ieSYXDRWFrTo4kPbmwPxCnaqt5qFGcqIPnPuXz6MXAkhN9XZUDq9ym/H0lm6O44dMZmAYftWMy9HOgSd2WAe6Eq4rxPW8kOEydCXlpL1xZdkff45aDR4PvUk7qNHY2FddWDQ6VWW7Ipj+qZjWGsseP2OVgy9IfCC4KQvKyP3hx/I+uJLtCkp2LZpg+fTT+HYt2+DCFgVqamkvvtujTenq6rKTweSmPpTFCrw1pDW3NspoEF8LcJ4pPP6RZZELmHG3hlM6TqFYS3PL6ts/WIekX9sZez8Jaiq7QW9p5w9bWnTO5CIHn7YOshauqhjSfsMy3+KBkb9CH7t6mSYnKJyVv6XwLLd8STlluDrbMsDnQPp1tSDNoEu0ubARKmqSuHvv5M27X0qEhNxHjQQ7xdfxMq3Zq1l4jKLePGHQ+yJy6ZPuBfT7ml7SfNRtbyc3LVryVr0ORUJCdiEh+P51JM43XZbvR0PdkE9Oh05y78lY9YsVL0ez3FP4/HII9VuTgfIK65gytpI1h9MpnOIG58M60CQuzzdLSRYXeBwxmEe/vVh+gb3ZcbNM8791FFSWMCip0YT0rY79u4DObE/w9B7qrU7bfsEEtzaQ5p3ivpxcrtho3odnvt3KDGXpbvjWXcwmXKtnm5N3RndPZR+rXzknDkTVx4XR+q0aRT9uQPr5s3wnfI6Dt0ufrj7yvR6lWV/x/PBr8ewtFB4bXAEw24MumQmR9Vqyfv5Z7IWLKQ8Lg7rZs3wfPIJnAcORLGsnyemS6KiSJ16ZnN6z56GzemXOd8PYPeJLCatOkB6QRkT+oXx5M3NsJR/G+IMCVZnFJQXcP/6+1FVlVV3rsLFxgUw9J7a+NnXRO9ajbXzQ9g6+knvKWEcR9fDD2PAvRk8tKZWz/0rrdCx4XAKX++O52BCLvbWGu7tFMBD3UIJ95VlbVOnLy4mc+EishcvRrGxwevZZ3AbOfKyMzY1cTqrmBd/PMjfJ7PpFebJB/e1q7KPk6rTkb9xI1kLFlAWE4tVSDCeY8fiMmTIdddQHX1RERlzPiV72TI07u74vvoKTgMHXnYZr0yrY+bmaBbtOEmohwOzhnWgfZBrndQnGi8JVhimvidvn8y209tYMmAJHbw7kJdRTOT2JI78lUh+ykJs7L3o++jLtOjii5WNPBou6tm+ZbD+OQi4AUauMsxY1YKk3BKW/x3Pd/8mkF1UTlMvBx7uFsK9NwTKUp8ZUFWVgk2bSPvgQ7SpqbjcdRfekydh6eVVa2Po9SrL95zmgw1HAXh1cAQjuwRXGWBUvZ6CbdvI/Owzyo4cxcrfH4+x/8Pl3nur3dt1LQp++82wOT0lxbA5/fnnL7s5HSAmrYDx3x3gSEo+I7sGM2VwBPbW0odQXEqCFfB99Pe8vfttxnecQH+Luzm8/XzvKU+/ZBIiV3D3C6/TrPPVT4kLcV1UFbZ/BH9Mg2a3wrBl133un6qq/BWbxdLdcWw9mgZAvwgfHu4eSo/mHrLx1kyUxcaS+t57FO/+G5uICHxfn1KnfaYSsot5efUh/orN4qZmHnx4X7tq9ySpqkrh9u1kfvYZpQcPYentjcfjj+F6//1Y2F175/KK1FTS3nuPgi1bsQkLw/ett7DvVP3m9LO1LN0dz7QNR3GwseTD+9rRv5XPNdcgTJ/ZB6vonGgeWfsYfYvvJiLlpnO9p1r38qd1zwDWzXiVkoJ8xnyy0CibKoUZ05bBumfh0EpoPxLunH1d5/4VlFbw495Elv0dz4mMItwdrBl+YxAjuwYT6CbL2uZCV1hI5rz5ZC9bhoW9PV4TxuM2bBiKpu5n4lVVZcWeBKZtOIpeVXl5YEtGdQ2pdo+qqqoU795N5vzPKP7vPzQeHniMeRS34cOxcKj5DxjXsjkdDK0kXvj+ENujM+gb7sWHQ9vh7SRHK4nLM/tgtW37HqJW5aDRWeHXzIW2fQJp2tELjaUFydHHWPH6ZG559Ak6DrizzmoQ4hJFWbDyQTi9G26ZAr0mX/MRNdFpBSzdHceafUkUletoH+TK6O4hDGrrJx3PzYiqquSvX0/a9OnoMrNwHToUr4kTsHSvnWXlq5GUW8LLPx5iR0wmXZu489HQdoR4XD4oFf/7L5mffUbRrt1oXF1xf2Q0bg8+iMbp8nsAS48cIWXqG5RGRtZ4czrA5qhUXl59mKIyLVMGRzCqW4jM5ooaMftgVZhTyp71p2jb59LeUz/P/oi4A3sZ+9kSrG3l4ExRTzJjYPn9kJ8M93wGbe676ltodXq2HEnj691x/H0yG2tLC+5s58/D3UNks60ZKj12jNR33qVk715s27bF9/Up2LWrmzYdNaWqKt//l8g7Px9Bq1d54fZwHrkp9IpPWJccOEDmZwso3L4dCycn3B8ahfvDD6NxvfDvtb6oiIxP55K9dCkad3d8XnkZ50GDrhiOisq0vPPzEb77N4HW/s7MHt6B5t7yAIeoObMPVtUpyMrk82fG0GnQXfR56LF6H1+YqVM7YOUosLCEESsgqMtVfXpGQRnf7TnN8n9Ok5pfSoCrHaO6hTDsxiDcHaQTtLnR5eWRMedTclasQOPigvek53G5994Gta0hJa+EV1cf5vfjGdwY6sZHQ9vTxPPKy3wlUVFkLVhAwZatWNjb4/bgSNwfeQRLDw8Kfvud1HfeMWxOHzYM70lX3pwOcCAhlwnf7Sc+u5gnejfj+f4tpOmtuGoSrKqx49sl/LtuNY/N+RwXb9moKOrB/uWwfjy4N4UHV4FbaI0+TVVV9p3OYenueDYcTqFCp9IrzJOHu4dyS0tvNNJjzeyoej15a9aQPmMmutxc3EaMwOu5Z9G4uBi7tCqpqsrqfUm8tT6KMq2eF24P59EeTWr0d7f0eDRZCxeQ/+tGFBsbbFu3pmTvXmzCmuP71ttX3JwOhhne+X+cYPa2GHycbJg5rAPdmnrUxpcmzJAEqypUlJWy6KlHCGrTjiHPv1qvYwszpNfD7+/CjhnQtI/h3D+7Ky/XlZTrWHcwiaW744lKzsfJxpKhnQN5qFsITb0c67xs0TCVHI4k9Z13KD10CLtOnfB9fQq2ERHGLqtG0vJLeW3NYbYeTadTsCsfDW1Pc++a/V0uO3mKrIULKdq9G7dRo/B4ZDRKDVo0nM4qZuKqA+yNz2FIe3/eubsNLnbSakRcOwlWVTi45Ve2fjGPYW99SGDL1vU6tjAzFSXw01MQtQY6jYbBM0Bz+f/U47OK+ObveFb9l0heSQUtfZ14qHsId3cIwMFG+uqYK21ODhkzPyH3hx/QeHjg88JknIcMaXQbrlVVZe2BZN5YF0VJhY5J/VvweK+mtT7zqqoqP+5L4s11USgKvHt3G+7qEFCrYwjzVNNgZTb/W6t6Pfs2rMWnaXMCwlsZuxxhygrTYcUISNoL/d+Bm56t9sk/vV5le0wGS3fF8Ud0BhaKwoDWvjzcPYQuTdwb3TdPUXtUnY7cVatInzUbfWEh7qNH4/nMODSOjXPWUlEU7u4YwE3NPZiyJpL3fz3GhshUPh7ajjCf2tlEnlNUzms/HWbD4VS6NHFn5gPtpeWIqHdmE6ziD+0nOzmRgc9Mkm9Wou6kH4VvH4DCDEPTz4hL23lodXr2nc5l29E0fo1M5XR2MV5ONjx7SxgjuwTj6yL9dMyJqqpo09Mpj4unPC6O8njD27Ljx6lISsK+a1d8p7yGTViYsUutFd5Otix86AbWH0rhjbWRDJ6zk/H9wniid9PrOpdvZ0wmk74/QHZROS8NaMnY3rU/GyZETZhNsNr76zocXN0I797T2KUIU3XiN1g1Gqzs4NFfDMfUnJFfWsGf0RlsO5rO78fTyS2uwEqj0K2pB5NvD2dAa195SsmEqaqKLifHEJzi4s+Fp/J4w+/VkpJz1yrW1liHBGMT0RLvyZNwGjDA5H4YVBSFIe39uamZB1PXRjJ903E2RqYy/f52tPS98lN+lZVW6Ji+6Thf7jxFMy8Hvhx9I20CGuZmfmEezCJYZSUmEHdgLz0eGIXGUjYvijrw31fwyyTwagkjV4JrEAnZxWw9msa2o+n8cyqLCp2Km70Vt4R7c2uED71beOIk5/aZFF1+/pnQdFF4iotDX1Bw/kJLS6wDA7EOCcGhaxesQ0OxDgnBOjQUS1/fBtU2oS55Otow/8Eb+OVQClPXRnLnpzt59pYwnurTDKsazF4dS81nwncHOJZawEPdQnh1UAR21tIcVxiXWQSr/RvXobGyol3/gcYuRZgavQ62TIXdc1Gb9+dA1xls/ruIbUe3E51WCEAzLwfG9GhCv1Y+dAp2k+WJRk5fXEz56dNVhidddvb5CxUFK39/rENCcLnzTqxDQ86FJyt//yseu2JOBrfzo1tTd95cf4SZW6LZFJXK9KHtaeVf9eyVXq/y1a44Ptx4DGdbS7565Eb6tvSu56qFqJrJB6uSwgKitv9GRM++2DvL9LCoReVFaH94HMvoDfzlfg8TTw4nPTISjYVCl1B3pgwOol+ED6E1aIooGhZ9eTkVCQnnZ58qhSdtWtoF11p6e2MdEoLTrbcawtOZ2SeroCAsbGyM9BU0Ph6ONnw6oiOD2/ox5adIhszdybi+zRnXt/kFy+Rp+aVM/v4gO2Iy6RfhzQf3tcPTUf6cRcNh8sHq8LZNaMvL6DRoiLFLESYiKbeEXfsP03n30wSXxfKm9mFWZ99Bn3Bv+rXy4eYWXtIvpwFR9Xp0eXnocnMr/cpDl5Nz0cfO/9JmZhr6kJ2hcXPDOjQUh+7dLwhP1sHBV3VosLiyAW186drEnbfWRzF7WwybolL5+P72tAlw4dfDKbyy5jClFTreu6cNI7sEm9z+M9H4mXSw0mm17N/0M8Ft2uMVHGrsckQjpderHErKY9vRNLYeTYfUw3xpPR03pZjvwz7i9u738VqoW432hIjroy8vR5eTiy4358zby/w6G5zy86G6fn2WlmhcXdG4uqBxdcUqOAjbdm2x8vbBusmZ8BQS0mA7m5sqNwdrZg3vyOB2/ry65jB3zfuLLqHu7D6ZRbtAFz4Z1oFm0ixXNFAmHaxi9uyiMCuTfo89bexSRCNTXK5lZ0wm246m89vxdDIKyrBQ4H++MUyy/xALW2csR61luJ9xD7xtbFRVRS0rQ19YiK6gAH1hEfqiQsP7hYXo8wvOBKPzs0naSrNManFxtfdW7O3PBSRLV1es/P3OhCY3w1s31zPvn/9l4egoMx4NWP9WPnQJdeftn4+wZn8iz/Rtzvh+YfJDjGjQTDpY7duwFldfP5p2vGKjVCFIzStl2zHDU3x/xWZSptXjZGNJ73Av+kV4c3vhWux/ewt828KIleDsZ+yS642qqqjFxegqBaFzYaiwCH1hQaXfn3mt6ML3DR8rAq328oMpChpn53Phx8rLG9uwFmjc3C4JRpXDkuxnMk0u9lbMeKA9793TBlsreeJPNHwmG6ySo4+REnOcWx59wmweXRZXR1VVIpPyDS0RjqURmZQPQJC7HSO7BtMvwocbQ92xVvSw6RXYswjCB8F9X4C16eyrKTlwgPzNW9AX5BvCUcGZIFRUaAhShYXoi4ou2HNUHcXWFgtHRzQODlg4OWHh6IhVUCAaB0csHM/+ckBz9vcOF73v5ITG2RlFI99AxYUkVInGwmSDlcbSkmadu9H65luNXYpoYPR6lembj7NmXxKp+aUoCnQKduPFAeH0i/AhzLvS8lBZAfwwBmI2Q/dnoP/bYNH4/4NXVZXif/aQuXABxbv/RrG2Prc0ZuHoiMbRAUsvr/PvO50NQRcFoTPhSOPogIWDg7QQEEKYPbM6hFkIgI2RKTz5zT76hnsxuJ0/fcO98Kjqce28RPh2mOGYmkHT4cbH6r/YWqaqKoXbt5O1YCElBw6g8fLE49ExuA17QJ5uE0KIy5BDmIWogl6vMmtrDE29HPj84c7Vn02WtA9WDIeKEnjwe2jeuGc+Vb2egs1byFy0kLIjR7H098P3jam43Huv7E0SQohaJMFKmJVfI1M5llrA7OEdqg9VR3+GHx8HBy946CfwaVW/RdYiVasl/5dfyFz0OeUnTmAdGorftGm43HmHLNsJIUQdkGAlzIZOrzJrazTNvR25o53/pReoKuz61HBETcANMGIFODbOYzL05eXkrV5D1hdfUJGYiE14OAEzZ+B0++2yMVwIIeqQBCthNn45nEJMeiFzR3a89Lw+XYXhEOV9X0Oru+GeBWBlZ5xCr4O+uJjc778na/FXaNPSsG3XDp9XX8Wxbx/p1ySEEPVAgpUwC2dnq8J9nBjU5qL+UyW58P1oOPkH9HwebnkdGlmLDl1BATnfriB7yRJ0OTnYd+mC//vTsO/eXQKVEELUIwlWwiysO5jEyYwiPnuwExaVZ6ty4mD5A5B9Au6aBx1HGa3Ga6HNySF76VJyvlmOvqAAh9698HzySew7dTJ2aUIIYZYkWAmTp9XpmbMtlgg/Z25v7Xv+hYQ9sGIE6CvgoTXQpLfxirxKFenpZH+1hJyVK1GLi3G67TY8nhiLXevWxi5NCCHMmgQrYfJ+OpDMqcwiFj50w/nZqsjVsOZJcPY3tFPwDDNukTVUnphE1pdfkPfjalSdDufBg/AcOxab5s2NXZoQQggkWAkTV6HT8+lvMbT2d+a2Vj6GD+anGNopBHaG4SvAwcO4RdZA2alTZC36nLz160FRcL37bjz+9zjWwcHGLk0IIUQlEqyESVuzL4n4rGK+eLjz+U3cR9aCqoMhcxt8qCo9fpyshQvJ/3Ujio0NbiNH4DFmDFa+vlf+ZCGEEPVOgpUwWeVaPXN+i6FdoAu3RlTqRxW1GnzagFcL4xV3BSUHD5K5YCGFv/+OhYMDHo8/jvsjo7H0aNhBUAghzJ0EK2GyftyXSGJOCe/c3eb8bFVeIiT8A7dMMW5xVVBVleI9/5K1cAFFu3ajcXHB89lncB81Co2Li7HLE0IIUQMSrIRJKtPqmPtbLB2CXOnTwuv8C1E/Gd62vtc4hVVBVVWKduwg87MFlOzfj8bTE+8XXsB12DA0jnIwshBCNCYSrIRJWvVfIkm5Jbx/b9sLG2RGrQbfduDRzHjFnaGqKgVbtpC1YCGlR45g6e+Hz+tTcL3vPixsbY1dnhBCiGsgwUqYnNIKHfN/j6VziBu9wjzPv5ATB0l7od+bRqrsPH1RESmvv07+hl+xDgnB7713cbnzThRra2OXJoQQ4jpIsBImZ+W/CaTklfLx/e0vmq06uwx4j3EKYoOI8gAAIABJREFUO6MsNpbE58ZTHheH1/PP4/HYGDkYWQghTIQEK2FSSit0zP8jli5N3Lmp2UVP0EWtBv9O4BZqlNoA8n75hZTXp2JhZ0fw4sU4dOtqtFqEEELUvsZ10qwQV/DtP6dJyy9jYr8WF85WZZ2AlIPQxjib1tXyclLffY/kSZOxbdmSJqt/lFAlhBAmSGashMkoKdcx/48TdG/qQfdLZqvWGN62urve66pITSVp/ARKDh7EffRovCdPQrGyqvc6hBBC1D0JVsJkLP8nnszCMuY/2OnSF6PWQGAXcA2q15qKdu0iadJk1LIyAmZ9gvOAAfU6vhBCiPolS4HCJBSXa/nsjxP0bO5JlybuF76YEQ1pkfW6DKjq9WQuWMDpxx5H4+FO6A8/SKgSQggzIDNWwiQs2x1PVlE5E/uHXfpi1BpAgVZ31Usturw8kl98icLt23G+4w783noTCwdp9CmEEOZAgpVo9ArLtCzYfoLeLby4IcT90guiVkNwd3D2r/NaSiKjSBo/nor0dHymvo7biBEXbqIXQghh0mQpUDR6X++KI6e4gon9qpitSj8KGcfqfBlQVVVyvv+e+JEjUfV6Qr9ZhvvIkRKqhBDCzMiMlWjUCkor+HzHSW5p6U3HYLdLL4hcDYoFRAypsxr0JSWkvv0OeWvW4HDTTfjP+BhLtypqEUIIYfIkWIlGbclfceQWVzChqtkqVTXsrwrpAU4+dTJ+eXw8ieMnUHbsGJ5PP43nuKeli7oQQpgxCVai0corMcxW9YvwoV2g66UXpEVCVgx0f7pOxi/Yto3kl18BCwuCFi7A8eab62QcIYQQjYcEK9FoffXXKfJLtVXPVsGZZUBNrS8DqlotGbNmkfXFl9i2bk3A7NlYBwbU6hhCCCEapxptXlcUZYCiKMcVRYlVFOXlKl4PVhTld0VR9iuKckhRlEG1X6oQ5+UVV/DljlPc3tqHNgEul15wdhmwSW9w8Ky1cbUZGZwe8xhZX3yJ67BhhHy7XEKVEEKIc644Y6UoigaYB/QHEoF/FUVZp6rqkUqXTQFWqar6maIorYANQGgd1CsEAF/uPElBmZYJ/VpUfUHKAcg5BT0n1tqYxXv3kjRhIrqCAvw+eB/Xu+v/eBwhhBANW02WArsAsaqqngRQFOU74C6gcrBSAeczv3cBkmuzyMYqvaCUT7bEcCgxl0Ft/bi/cyDeTrbGLqvRyykqZ/FfcQxq60uEn3PVF0WuBgtLiLjzusdTVZXsr78mffrHWAUGEPrF59iGh1/3fYUQQpiemgSrACCh0vuJQNeLrnkT2KwoyrOAA9CvqhspijIWGAsQHBx8tbU2GiXlOj7fcZIF209QodPTys+Z6ZuO88mWaPq38mFk12B6NPPEwkJ6HF2Lz3ecpKhcy/hbq5mtUlWI+gma9gX7KhqGXgVdYSEpr02hYNMmnPr3w2/aNDROTtd1TyGEEKarJsGqqu/+6kXvjwCWqKo6Q1GU7sAyRVHaqKqqv+CTVHURsAigc+fOF9+j0dPrVVbvT+LjTcdJzS9lQGtfXh7YklBPB05mFPLdvwn8sDeRXyNTCXa3Z3iXIIbeILNYVyO7qJwlu+IY3NaPcN9qAk7SXsg7DX0u2Q54VUqjo0l6bjzlCQl4v/AC7mMelYafQgghLqsmwSoRCKr0fiCXLvU9BgwAUFV1t6IotoAnkF4bRTYGu05k8t4vR4lKzqd9oAtzRnS84DDgpl6OvDoogkm3tWBTVBrf/hPPRxuPM3NzNLe19mFEF5nFqomFf56gpEJX/ZOAYNi0rrGGloOveZy89etJmfoGFg4OBH+1GIcuXa75XkIIIcxHTYLVv0CYoihNgCRgODDyomtOA7cCSxRFiQBsgYzaLLShik0v5INfj7L1aDoBrnbMHt6BO9v5VxuQbCw1DGnvz5D2/pzIKOS7Paf5YW8iGw6fn8W6/4YgvJxs6vkrafgyC8tYuiueu9r709y7mtkqvd4QrJrdCnZV9La6An15OekffEDOtyuw63wDATNnYuXtfZ2VCyGEMBdXDFaqqmoVRXkG2ARogMWqqkYpivI28J+qquuAScDniqJMxLBM+IiqqsZd6ovZCj9PhNHrwL1Jrd8+q7CMWVtj+HbPaeysNLw4IJwxPZpga1XzrtvNvBx5bXArJt8ezsbIVFbsOS2zWJexcPsJyrQ6nrv1MrNViXsgPwlufeOq71+RnEzihImUHjqE+5gxeE+cgGJldR0VCyGEMDc1ahCqquoGDC0UKn9saqXfHwF61G5p18nG0bDPJjOmVoNVaYWOr/6KY/7vsRRX6BjZJZjx/cLwdLz2GSYbSw13dQjgrg4BMotVjfSCUpb9Hc/dHQNo6uVY/YVRa0BjA+EDr+r+hTt2kvzCC6gVFQTMmY3zbbddZ8VCCCHMkel2Xvc888RYZjS0uP5vkqqqsu5gMh9tPE5Sbgm3tvTmlUEtq1+SukZnZ7Em3RbOpqhLZ7FGdgnhpmYeZjeLteCPk1ToVJ675TKzVXqd4WnAsP5gW00bhouoej2Z8z8jc948bJo3J2DObGya1P4MpxBCCPNgusHK3h3s3A3B6jr9G5fNu78c5WBCrqF1wtB23NS89rp5V8XWqvpZrBAPe4bfGMzQGwLNYhYrLb+Ub/6J596OAYR6OlR/4endUJgKre+p0X21OTkkv/gSRTt24HLXEHzfeAMLe/taqloIIYQ5Mt1gBYZZq6zYa/70uMwiPvj1GBujUvFxtmH60Hbc2ykQTT3PFlU1i/XhxmPM2HzcLGax5v8ei16v8uzlZqvAsAxoaQctBlzxniWHD5M4fjy6jEx833wT12EPSCsFIYQQ183Eg1VziN501Z+WW1zOnG2xLPs7DiuNBc/3b8HjvZpgb23cPy5znMVKySthxZ4Eht4QSLDHZWaTdFo4staw7GtzmT1YGEJV/KiH0Hi4E/Ltcuzatq3lqoUQQpgrEw9WLWD/N1CSA3ZuV7y8XKtn6e44Pv0tloLSCh7oHMTz/Vvg7dzwGnhePIv17T+GWayZW45zWytfRnQJNolZrHm/x6KiMq5v88tfGP8XFGVA63sve1lFWjqJ457B0tOT0FUrsfTwqMVqhRBCmDvTD1YAmbEQdGO1l6mqysbIVD7YeIz4rGJ6hXny6qCI6s+ha0AunsVa8c9pftyXyC+HUxr9LFZSbgkr/03ggc5BBLlfYe9T1GqwcoCw6h9U0JeWkvjss+gKCwld8a2EKiGEELXOtIOVx5k9OZnR1Qar/adzeO+Xo/wXn0MLH0eWPHojfcIbZ0PIZl6OTLnD0BerqlmsMT1DuSHk+s7Oq09zf4tFQbnybJWuAo6sg/ABYF11AFNVlZSpUyk9dIiAT+fIIcpCCCHqhGkHK7cQsLCCrJhLXkrILuajTcdZfzAZT0drpt3Tlgc6B2KpsTBCobWr8ixWbPqZvVhnZrGGdQ7i1UERuNg37MaXCdnFfP9fAiO7BuPvanf5i0/9CSXZl10GzP7yS/LXrcdr/HM49+9fy9UKIYQQBqYdrDRWhuagmeeDVX5pBfN+j+Wrv+JQgGf6NufJPs1wtDHNP4rm3oZZrEm3hTNrWzRf7DjFtmPpvDmkFYPb+jXYJ+Hm/haLhYXC032uMFsFhmVAaydo3q/Klwv++IP0GTNxGjgAjyefrOVKhRBCiPNMM01U5tkCMmOo0OlZsec0s7bGkF1Uzr2dAnjh9nD8XK4wG2Ii7Kw1vDIwgjvb+fPK6sM88+1+1rRM4p2721x5RqiexWcV8cO+RB7qFoKvyxUeHNCWw9H10HIQWF16bVlsLMmTJmMT0RL/adMabJAUQghhGkw+WKkeYajRmxj0yW/EZJbRrak7Uwa3ok2Ai7FLM4o2AS6sefomluyKY8bmaPrP3M6LA1oyqltIvffnqs6cbbFYWig83afZlS8++QeU5lW5DKjLzSXh6XEodnYEzZuHhV3DCpBCCCFMT+PfUHQZkUl5LIiywEJfgZ8+jc8f7syK/3Uz21B1lqXGgsd7NWXzxN50CnHjjXVRDF2wi+OpBcYujVOZRazZn8iobiE1a3MRtRpsXKBZ3ws+rGq1JE6ciDYlhcBP52Dl51dHFQshhBDnmWywWn8wmTvn7mR3nuEpuMV3utK/lY8sBVUS5G7P0jFd+GRYe+Kzirnj0x3M2Hyc0gqd0Wqasy0Ga0sLnry5BrNV2jI49gtE3AGWF7aTSPvgQ4p3/43vm29i37FjHVUrhBBCXMhkg1XvFl4807c5nz43DADL7Gs/2saUKYrCPR0D2fr8zdzZzp9Pf4tl0Jwd/HMyq95riU0vZO2BJB7uHlqzvlux26As/5JlwJxVq8j55hvcR4/G9b7LNwwVQgghapPJBisXOysm3RaOi5sXOHhd8GSguJS7gzUzh3Vg6ZguVOj0DFv0N6+sPkReSUW91TBnWwy2Vhqe6N20Zp8QtdrQUb/pzec+VPzff6S+8y4OPXvi/cLkOqpUCCGEqJrJBqsLnHkyUFxZ7xZebJrQm7G9m7Ly3wT6zdzOr4dTUFW1TseNTitg/aFkRt8UiodjDWarKkrg+K8QcaehrQZQkZRE4rPPYR0QQMDMGSiWJv9shhBCiAbGPIKVR3ND93VRI/bWlrw6KIK143ri7WTDU8v3MXbZXlLzSutszNnbYrC30jC2Vw1nq2K2QHnhuWVAfVERCU+PQ9VqCZw/H41zwz+OSAghhOkxj2Dl2cLQmbuo/vcNNWZtA11YO64HrwxsyY6YDPrN3M6y3XHo9bU7e3UsNZ9fDqXwaI8muDlY1+yTolaDvSeE9kLV60l++WXKYmIImDkTm6ZNarU+IYQQoqbMJ1hBlUfbiMuz1FjwxM3N2DzhZjoEufL62ijuX7ibmLTaa80wa0sMTjaWPN6rhoGovAiiN0GrIaCxJHPefAq2bMX7xRdw7NWz1uoSQgghrpaZBKszx6LIPqtrFuxhz7LHujDj/vacyChk0JwdzNwSTZn2+lozRCXnsTEqlUd7NsHVvoazVdGboKIYWt9D/saNZM6bh8s99+A+evR11SKEEEJcL/MIVq4hoLGWfVbXSVEU7rshkG3P38zgtn7M2RbDoNk7+Dcu+5rvOWtrDE62ljzW8yqW76LWgKMPpUXuJL/8CnYdOuD71pvSo0wIIYTRmUewstCAezOZsaolHo42zBrekSWP3khphZ77F+zmtTWHyS+9utYMhxPz2HIkjcd7NsXFzqpmn1RWADGb0QYNIOHZ59C4uhL46RwsrGs42yWEEELUIfMIVgCeYbLHqpb1Cfdm88TePNazCSv2nKb/zO1sjEyt8efP2hqNi50Vj/YMrfmgxzeiLyslceUpdDk5BM6bi6WX19UXL4QQQtQBMwpWLSD7FGjLjV2JSXGwseT1O1qx5ukeuDvY8OQ3e3li2X+k5V++NcOBhFy2HUtnbO+mONvWcLYKUCNXk3rQn5Ijsfi/Pw271q2v90sQQgghao0ZBaswUHWQE2fsSkxS+yBX1j3Tg5cGtOSP4xn0m7Gdb/6Or7Y1w6yt0bjZWzH6ptCaD1KaR84vO8mLBo+nnsR54MDaKV4IIYSoJeYVrEA2sNchK40FT/VpxqYJvWkb6MKUnyIZtmg3sekXtmbYG5/DH8czGNu7GY42Ne+OXrhqLmn7HHDs0QmvZ5+t7fKFEEKI62Y+wcpDglV9CfV0YPnjXfloaDui0woZNHsns7aeb80wa2s07g7WPNw9pMb3LDt1iqSZ32LjphAweyGKhfn81RVCCNF4mM9harbO4OgLWbHGrsQsKIrCA52D6Bvuzds/H2HW1hh+OZTCiC7B7IjJ5NVBLXGo4WyVrqCAxKeeRFG1BD4zEAtHxzquXgghhLg25vVjv2eYzFjVMy8nGz4d0ZGvHrmR4nIdb/98BE9HGx7qFlqjz1d1OpKen0R5QiIBPbKxvvmhui1YCCGEuA7mM2MFhmAVuRpUFaSZZL3q29LQmmHRnyfpEOSKnbWmRp+XPmMmRTt24DvQF4cQDfh3quNKhRBCiGtnZsGqBZTmQlEmOErvo/rmYGPJxP4tanx97k8/kb14MW4P3Iub5jNo/awEYiGEEA2aeS0Fygb2RqPkwAFSX5+Kfbdu+AxuamiV0eZeY5clhBBCXJZ5BauzLRekA3uDVpGaSsKzz2Lp60vAJzNRjq01HEnk287YpQkhhBCXZV7ByiUILG3lzMAGTF9SQuK4Z1CLSwiaPw9LKy3E7YDW98gyoBBCiAbPvIKVhQV4NJdg1UCpqkrKa1MoPXIE/+nTsQkLg6NrQdXLMqAQQohGwbyCFUjLhQYsa+Ei8jdswGviRJxu6Wv4YOQaw0MH3q2MW5wQQghRA+YXrDzCIDcetGXGrkRUUrBtGxmzZuF8xx14/O/xMx9Mhfi/oPW9sgwohBCiUTC/YOXZwrC0lH3S2JWIM0qjo0l+4UVs27bF7913UM6GqCNrAdWwv0oIIYRoBMwwWEnLhYZEm5ND4tPjsHBwIHDup1jY2p5/MXK1YQnQu6XxChRCCCGugvkFK4/mhreygd3o1IoKksZPQJueTuC8uVj5+Jx/MS8JEv42LAMKIYQQjYR5dV4HsHEE5wAJVg1A6rRpFO/Zg/9HH2LX7qIeVUd+MryVZUAhhBCNiPnNWMGZlguyFGhMOStWkLviOzwefwyXIUMuvSBqDfi2Bc/m9V+cEEIIcY3MM1h5toCsWMNhzKLeFf2zh9T3puF48814TZx46QW5pyHxX1kGFEII0eiYb7Aqy4fCNGNXYnbKExJIGj8e65AQ/Gd8jKLRXHpR1BrDW1kGFEII0ciYabCSDez1TVVV8jdsIH70aFRVJWj+PDSOjlVfHLUG/DuCe5P6LVIIIYS4TmYarFoY3so+q3pRvHcvccOHk/T8JDROzgQvWoh1SEjVF2efhOT9sgwohBCiUTK/pwIBnPzByl5mrOpY2alTZMycScGWrVh6e+M3bRoudw2pevnvrHPLgHfXT5FCCCFELTLPYHX2MOYsCVZ1QZudTea8+eSsXImFtTVeE8bjPno0FnZ2V/7kqDUQeCO4Btd9oUIIIUQtM89gBYblwMQ9xq7CpOhLS8leuoysRYvQl5Tg+sD9eI0bh6WnZ81ukBkLqYfh9vfrtlAhhBCijphxsAqDyB+hogSsajCTIqql6vXkr19P+qzZaFNScLzlFrwnT8KmadOru9HZZcBWd9V+kUIIIUQ9MO9ghQpZJ8C3jbGrabSK/v6btI8+ouzIUWzbtMH/ww9w6NLl2m4WtRqCu4NLQO0WKYQQQtQT8w1WHpUOY5ZgddXKYmNJn/4xhdu3Y+Xvj//06TgPHoRicY0PmqYfg/QjMPCj2i1UCCGEqEdmHKzO9LLKijVuHY2MNiODjE/nkvvDD1g4OOD9wmTcRo3Cwsbm+m4ctQZQZBlQCCFEo1ajYKUoygBgNqABvlBV9YOLXv8E6HvmXXvAW1VV19ostNZZ24NLsPSyqiF9cTFZX31F1peLUcvLcRv1IJ5PPYWlm9v131xVDcuAoT3Byff67yeEEEIYyRWDlaIoGmAe0B9IBP5VFGWdqqpHzl6jqurEStc/C3Ssg1prn2dz6WV1BapOR96aNWTMnoM2IwOn22/H+/mJ1Tf4vBZpUYaA2/WJ2runEEIIYQQ1mbHqAsSqqnoSQFGU74C7gCPVXD8CeKN2yqtjni1g3zLDjImiGLuaBkVVVYp27iT9o+mUxcRg16EDAbNnY9+pDjJz1BpQLCBClgGFEEI0bjUJVgFAQqX3E4GuVV2oKEoI0AT4rZrXxwJjAYKDG0ADSI/mUFEE+cnyJFolpUePkj59OkW7dmMVHEzArFk43X4bSl2Ez7PLgE16g6NX7d9fCCGEqEc1CVZVfTdVq7l2OPCDqqq6ql5UVXURsAigc+fO1d2j/pw9MzArRoIVUJGaSsbsOeT99BMaZ2d8Xn0Ft+HDUayt627Q1EOG8wF7jK+7MYQQQoh6UpNglQgEVXo/EEiu5trhwLjrLarenDuMOQaa9jFmJUalKywk64svyF7yNeh0uI95FM8nnkDj7Fz3g0euBkUDEUPqfiwhhBCijtUkWP0LhCmK0gRIwhCeRl58kaIo4YAbsLtWK6xLTr5g7Wi2G9jVigpyf/iBjE/nosvOxvmOO/CaMAHrwHqavTu7DNi0D9i718+YQgghRB26YrBSVVWrKMozwCYM7RYWq6oapSjK28B/qqquO3PpCOA7VVWNv8RXU4pi6MBuZi0XVFWl8PffSZ/+MeWnTmF/4414L1yIXdt6bpSavA9yT8PNL9XvuEIIIUQdqVEfK1VVNwAbLvrY1Ivef7P2yqpHHmEQv8vYVdSbksOHSf/wI4r/+w/rJk0InD8Px75962Zj+pVErgYLK2g5uP7HFkIIIeqA+XZeP8uzBRxeBeVFYO1g7GrqTEVqKukfzyD/55/RuLvj+8ZUXIcORbGyMk5Bej1E/QTNbgG7WmgyKoQQQjQAEqw8z5wZmBULfu2NW0sd0ZeWcnrMY1QkJ+Px5BN4PP44GkdH4xaV9B/kJ8Ktrxu3DiGEEKIWSbA6G6wyY0w2WKXPmEn5yZMEL/4Sh5tuMnY5BpGrQWMD4YOMXYkQQghRayyMXYDRuTcDFJN9MrDwr7/IWbYMt4ceajihSq+HIz9B835gWw8tHYQQQoh6IsHKyhZcTfMwZl1eHimvvoZ106Z4T3re2OWcl/A3FKRAm3uNXYkQQghRq2QpEAwb2LNMb8Yq9Z130WZlETp3Lha2tsYu57zI1WBpCy1uN3YlQgghRK2SGSswBKvMWMMSlYnI//VX8n/+Gc+nnqz//lSXo9fBkbUQdhvYOBm7GiGEEKJWSbAC8GwO2hLITzJ2JbWiIi2dlDffwrZdOzyfeMLY5Vwo/i8oSpdlQCGEECZJghVUOjOw8e+zUlWVlNdeQy0rw//DD1AsG9hqb+RqsLI3zFgJIYQQJkaCFVx4GHMjl/vddxTt3In3iy9g06SJscs5T1Xhn4WwbylE3GnSzViFEEKYrwY2nWEkDl5g49LoN7CXx8WR9tF0HHr2xG3ECGOXc562DH55HvZ/A+GDYfAMY1ckhBBC1AkJVmAShzGrWi1JL72EYm2N33vvGefsv6oUpMHKUZC4B3q/CH1eAQuZKBVCCGGaJFid5RkGJ7cbu4prlvX555QePETAzBlY+XgbuxyDpL3w3SgozYX7v4bWdxu7IiGEEKJOydTBWZ5hUJAMZQXGruSqlURGkTFvPs6DB+M8qIEcEXNwJSweCBaW8NhmCVVCCCHMggSrsxrpBnZ9aSnJL72EpYcHvlMbwIHGeh1sngJrxkJQFxj7O/i2NXZVQgghRL2QpcCzPM4cxpwVCwGdjFvLVUifOZPyEycI+vILNC4uxi2mJAd+eAxObIMb/wcD3geNlXFrEkIIIeqRBKuz3JuAomlUG9iLdu8mZ+ky3B58EMcePYxbTMZxWDECck/DnbPhhkeMW48QQghhBBKszrK0AbeQRrMUqMvPJ/mVV7Fu0gTvyZOMW8zxjfDj44YDrUevh5Duxq1HCCGEMBIJVpV5tmg0wSr13XfRZmQQ+t0KLOzsjFOEqsLOmbDtHfBrB8O/BZdA49QihBBCNAASrCrzDIMTvxs2YFtojF1NtfI3biJ/3Xo8x43Drq2RNoaXF8PacRC1GtreD3fOAWt749QihBBCNBASrCrzCANdGeQlgFuosaupUkV6OqlvvIFtmzZ4PmmkA5ZzE+C7kZB6GPq9BT3GG5qsCiGEEGZOglVllVsuNMBgpaoqKVOmoC8rw/+jj1CsjPDEXfwuWPkQ6Cpg5CpoIYcpCyGEEGdJH6vKPM+0XGig+6xyV66i6M8deE+ejE1TIxyw/N9i+PpOsHOF/22TUCWEEEJcRGasKrP3ADu3BtlyoTw+nrQPP8ThpptwG1nPByxry2HjS4ZgFXYb3Pu5IVwJIYQQ4gISrCpTlAb5ZKCq1ZL80ssoVlb4vT8NpT4PMS7MgO9HQ/xf0GMC3Dq1QW/sF0IIIYxJgtXFPMIgdouxq7hA1hdfUnLgAP4ff4yVj0/9DZxyyLBJvSgD7vsS2g6tv7GFEEKIRkj2WF3MMwwK06A0z9iVAFASFUXG3Lk4DxqIyx2D62/gyNXw5W2g6mHMRglVQgghRA1IsLrYuQ3sscatA9CXlRkOWHZ3x3fq1HoaVA/b3oYfHgW/9jD2D/DvWD9jCyGEEI2cLAVe7FzLhWgIvMGopWTM/ITy2BMEff45Gtd62Cxemg+r/wfRG6HTaBj0MVha1/24QgghhImQYHUxt1CwsDT6k4FFf/9D9tdf4zZyBI69etb9gFknDIcoZ58wBKobH5emn0IIIcRVkmB1MY0VuDWBLOM9GagrKCD5lVewDgnBe/Lkuh8wdiv8MMYQKB/6CZr0qvsxhRBCCBMkwaoqRm65kPbue2jT0wn9djkW9nV4/p6qwu65sGUqeLcyHKLsFlJ34wkhhBAmToJVVTybG1ou6LSgqd8/ovzNm8lbuxbPp5/Crn37uhuoogTWj4dDK6HVXXD3Z2DtUHfjCSGEEGZAglVVPFuArhxy48GjWb0Nq83IIHXqG9i2bo3nU0/V3UD5yfDdg5C8D/pOgd6TZT+VEEIIUQskWFWl8mHM9RSsDAcsv46+pAT/jz6suwOWE/bAylFQXmRY+mtZj72xhBBCCBMnfayq4tHc8LYeN7Dnfv89hdu34z1pEjbN6ijM7f8GlgwGK3t4fKuEKiGEEKKWyYxVVezdwd6z3loulJ8+TdoHH2LfvRtuox6sm0F2zYXNr0HTvjB0seFrFEIIIUStkmBVHc+weum+rup0hgOWNRr8p9XRAcuqCnsWQWgvePCHet+QL4QQQpgLWQqsjmdYvcxYZX3xJSX79+M79XWs/PzqaJBYw0b81vdIqBJCCCHqkMkGK1WvR9UQHtqQAAAgAElEQVTrr/0Gni2gOBOKs2uvqIuUHj1Kxty5OA0YgPMdd9TZOMRsMbwN6193YwghhBDCdINV8X//EdP7ZpJfepm8n39Bm5NzdTfwOHMYc1bdLAfqy8pIfvFFLF1d8X1jKkpdtjuI3QKe4eAaXHdjCCGEEMJ091hZODjg0LUrhdu3k7d2LSgKtm3a4NirJw49e2HXri2K5WW+fM8zwSozGoK61Hp9GbNmUxYTS9CihVi6udX6/c8pL4a4v6DL/+puDCGEEEIAJhys7Fq3JmDGx6g6HaVRURTu2EHRjp1kLlhI5vzPsHB2xqF79zNBqydWvr4X3sA1BCys6uRom6I9e8hesgTX4cNw7N271u9/gbgdoCuD5v3qdhwhhBBCmG6wOkvRaLBr1w67du3wGjcOXV4eRbt3nwtaBZs2AWATFoZDr1449uyBXefOWFhbG5qD1nKw0hUWkvLyK1gFB+Hz4ou1eu8qxWwBKwcIuanuxxJCCCHMnMkHq4tpXFxwHjAA5wEDUFWVspgYinbspHDnDnKWLSN78WIUOzscunTBwcEFx+xjWKlqre2BSntvGhWpqXV/wDIY2izEboEmvcHSpm7HEkIIIYT5BavKFEXBtkULbFu0wOOxMeiLiijas4einX9RuHMHhfEJpAFWf96GY69eOPTshUPXLlg4XNthxQVbt5K3Zg0eTz6BXYcOtfvFVCXrBOTEwU3P1v1YQgghhDDvYHUxCwcHnPr25f/t3Xl8FeXZ//HPRQiLhD2ICrIoQXaiIKCAQq0oSsVHEaxWpRSVR0Fqf9jiRhGXolLqhvKoILVFAUGBR+2jIEvLpgQJOwpIQGTflMiWkOv3x0zCIWQDzpm5Sa7365VXkrPNN2fuzNxn5p7rrti5MwBH/28U6f94hp/LtWH/1Gnse+99iI/nnMsu88ZmdexI2YYNi3Q0K3P3brY9OYSyTRpT44EHYv2neNb7ZRYaWJkFY4wxJgjWsSpAmWZtqZZ0kGq39yLrorc49PXX3tisefPZOeKvMOKvlK5RgwodOngdrSuvJK5KlZNeR1XZ9uQQsn7+mVovvICUKRPMH7BuhlePq2rdYJZnjDHGlHDWsSpI9mTMu7+lVKMbqNCuHRXatYNHHiFjx46cU4YHZs3ix48+glKlKN+8eU5Hq1zz5khcHD9OmUL67NnUfHQwZRs0CCb70YOQNg8u7xvM8owxxhhjHasCla8CCTXzvDIwvmZNqtx6C1VuvcUr6bBiBen+IPjdr7/O7lGjKFW5MhWuvIKf5/6bc9q1o+pddwWXPW2eV2YhycosGGOMMUGxjlVhqifBnoJLLkhcHOWTkymfnEyNAf3J3LePgwsX5nS0pEwZLnju2dhMsJyfdZ9D/DlQt31wyzTGGGNKOOtYFSYxCVZPPaWnlK5alUo33EClG25AVdGMDK8uVlCszIIxxhgTimI7V2DUJCbBoX3w857TerqIBNupguNlFqzaujHGGBOoInWsROR6EflGRNaLyOB8HtNTRFaLyCoReS+6MUOU2ND7vvvbcHOciuwyC0lWZsEYY4wJUqEdKxGJA0YBXYEmwK9FpEmuxyQBjwLtVbUp8PsYZA1H5GTMZ4t1M7yxYVXrhZ3EGGOMKVGKcsSqDbBeVb9T1aPABKB7rsfcC4xS1X0AqrozujFDVPlCiCtb6AB2Z2SXWbCjVcYYY0zgitKxqgV8H/H7Fv+2SA2BhiIyX0QWicj1eb2QiNwnIikikrJr167TSxy0UnFePasoT8YcM9llFmx8lTHGGBO4onSs8pqvRXP9XhpIAjoBvwbeFpGTSpCr6puq2lpVW9eoUeNUs4Yn8SzqWK2fYWUWjDHGmJAUpWO1Bbgw4vfawNY8HjNNVTNUdSPwDV5Hq3hIbOhdZZd5JOwkhVs3A+p1hPhyYScxxhhjSpyidKwWA0kiUl9EygC3A9NzPWYq0BlARBLxTg1+F82goUpsCHoM9m4MO0nB9myAfRttfJUxxhgTkkI7VqqaCfQHPgPWAJNUdZWIDBORm/yHfQbsEZHVwGzgEVU9vcJPLsqeM9D1Aezr/DILNr7KGGOMCUWRKq+r6qfAp7luGxLxswJ/8L+Kn7Ol5ML6GV4nsFr9sJMYY4wxJZJVXi+KshWh4gWwe33YSfKXcci7IrCBnQY0xhhjwmIdq6JKbOD2Eau0eZB5GJLsNKAxxhgTFutYFVViQ6/kguauNOGIdTOgdHmo2yHsJMYYY0yJZR2roqqeBEd+hJ8dLWy6fgbUtzILxhhjTJisY1VULg9g37MB9n5n46uMMcaYkFnHqqgSG3rfXazAvn6m993GVxljjDGhso5VUVWq5Y1hcrFjtW4GVLsYql0UdhJjjDGmRLOOVVGVKuXmlYEZhyDtP1Zt3RhjjHGAdaxORfUk96qvp833yizY+CpjjDEmdNaxOhWJDWHfJsg4HHaS49bPgNLloF77sJMYY4wxJZ51rE5FYhKg3hV4rlg3A+p1hPjyYScxxhhjSjzrWJ0K10ou7P0O9m6w8VXGGGOMI6xjdSqqN/C+u3Jl4Dq/zEIDK7NgjDHGuKB02AHOKmUqQKXa7gxgXz/DK7FQ/eKwkxhjjImCjIwMtmzZwuHDDo3lLWHKlStH7dq1iY+PP63nW8fqVCUmuXEqMOMwbPwPXHZ32EmMMcZEyZYtW6hYsSL16tVDRMKOU+KoKnv27GHLli3Ur1//tF7DTgWeqsSGsHt9+JMxb5oHmYdsfJUxxhQjhw8fpnr16tapComIUL169TM6Ymgdq1OVmARHD8CB7eHmWDfTL7PQIdwcxhhjoso6VeE60/ffOlanypUrA9fP8DpVVmbBGGOMcYZ1rE5Vdb9jFeYA9r0bYc96q7ZujDHmrLN//35ef/31nN+3bt1Kjx49QkwUXdaxOlWVLoD4CuGWXFjvl1mw8VXGGGPOMrk7VhdccAGTJ08OMVF02VWBp0rEvzIwxI7VuhlQtb6VWTDGmGLsqf9dxeqtP0X1NZtcUIk//6ppgY9JS0uja9eudOjQgQULFlCrVi2mTZvG1q1befDBB9m1axfnnHMOb731Fo0aNWLDhg3ceeedHDt2jK5duzJy5EjS09NJT0+ne/fu7Nu3j4yMDJ555hm6d+/O4MGD2bBhA8nJyVx77bU8+OCDdOvWjZUrV9K2bVvGjh1L06Zexk6dOvHXv/6VRo0aMWDAAFasWEFmZiZDhw6le/fuUX1vosWOWJ2OMDtWGYdh47/taJUxxpiYWbduHQ8++CCrVq2iSpUqTJkyhfvuu49XX32VJUuWMGLECB544AEABg4cyMCBA1m8eDEXXHBBzmuUK1eOjz76iK+//prZs2fz//7f/0NVGT58OBdffDGpqam8+OKLJyz39ttvZ9KkSQBs27aNrVu30qpVK5599ll+8YtfsHjxYmbPns0jjzzCzz//HNwbcgrsiNXpSGwIKz6AowehzDnBLnvTfK/Mgo2vMsaYYq2wI0uxVL9+fZKTkwFo1aoVaWlpLFiwgNtuuy3nMUeOHAFg4cKFTJ06FYA77riDQYMGAV5NqMcee4x///vflCpVih9++IEdO3YUuNyePXty7bXX8tRTTzFp0qSc5X3++edMnz6dESNGAF5Zis2bN9O4cePo/uFRYB2r05E9tc3eDXBe82CXvX4mxJW1MgvGGGNipmzZsjk/x8XFsWPHDqpUqUJqamqRX2P8+PHs2rWLJUuWEB8fT7169QqtD1WrVi2qV6/O8uXLmThxIv/zP/8DeJ20KVOmcMkll5zeHxQgOxV4OhIbet/DKLmwzi+zEPSRMmOMMSVWpUqVqF+/Ph988AHgdXSWLVsGQLt27ZgyZQoAEyZMyHnOjz/+yLnnnkt8fDyzZ89m06ZNAFSsWJEDBw7ku6zbb7+dF154gR9//JHmzb2DF9dddx2vvvoq6hfnXrp0afT/yCixjtXpqH4xIF4F9iDt3eiVeUjqEuxyjTHGlHjjx49nzJgxtGzZkqZNmzJt2jQAXnrpJUaOHEmbNm3Ytm0blStXBuDOO+8kJSWF1q1bM378eBo1agRA9erVad++Pc2aNeORRx45aTk9evRgwoQJ9OzZM+e2J598koyMDFq0aEGzZs148sknA/iLT49oSFOztG7dWlNSUkJZdlS81Bxqt4EeY4Jb5ldvwaeDYMDXdkWgMcYUQ2vWrHFy3FBBDh48SPny5RERJkyYwPvvv5/T6Tpb5bUeRGSJqrYu7Lk2xup0JTYM/lSglVkwxhjjmCVLltC/f39UlSpVqjB27NiwI4XKOlanq3oSbFoAWVlQKoAzqtllFi67K/bLMsYYY4qoY8eOOeOtjI2xOn2JSZBxEA5sDWZ5VmbBGGOMcZ51rE5XzpWBARUKtTILxhhjjPOsY3W6Ev3JmIPqWFmZBWOMMcZ51rE6XQk1oWylYAaw70vzyyzYaUBjjDHGZdaxOl0iXgX2PQEcsVo3w/tu46uMMcacBUaPHs27774LwLhx49i69fh45L59+7J69eqwosWcXRV4JhIbQtp/Yr+c9TOhaj0rs2CMMeas0K9fv5yfx40bR7NmzXImaH777bfDihUI61idicQkWD4BjqRD2YTYLCO7zELynd5RMmOMMSXDvwbD9hXRfc3zmkPX4QU+JC0tjeuvv562bduydOlSGjZsyLvvvsvChQsZNGgQmZmZXH755bzxxhuULVuWwYMHM336dEqXLk2XLl0YMWIEQ4cOJSEhgXr16pGSksKdd95J+fLlWbhwIV27dmXEiBEsXryYjRs38sILLwBeB2zJkiW8+uqr/POf/+SVV17h6NGjtG3bltdff524uLjovhcxYqcCz0T2APY9MZzaZvMCr6yDja8yxhgTkG+++Yb77ruP5cuXU6lSJUaOHEnv3r2ZOHEiK1asIDMzkzfeeIO9e/fy0UcfsWrVKpYvX84TTzxxwuv06NEjZ0qb1NRUypcvf8J9H374Yc7vEydOpFevXqxZs4aJEycyf/58UlNTiYuLY/z48YH97WfKjlidiciSCxckx2YZ67LLLHSMzesbY4xxUyFHlmLpwgsvpH379gD85je/4emnn6Z+/fo0bOjt9+655x5GjRpF//79KVeuHH379uXGG2+kW7duRV5GjRo1uOiii1i0aBFJSUl88803tG/fnlGjRrFkyRIuv/xyAA4dOsS5554b/T8yRqxjdSaqXQRSKrYD2NfPgHrtrcyCMcaYwEgRh56ULl2ar776ii+++IIJEybw2muvMWvWrCIvp1evXkyaNIlGjRrxX//1X4gIqso999zDX/7yl9ONHyo7FXgmSpeFKnVjV3Jh3ybvte1qQGOMMQHavHkzCxcuBOD999/nl7/8JWlpaaxf7w19+cc//sHVV19Neno6P/74IzfccAMvvfQSqampJ71WxYoVOXDgQJ7LueWWW5g6dSrvv/8+vXr1AuCaa65h8uTJ7Ny5E4C9e/eyadOmWPyZMWFHrM5UYkPYHaMxVuv9Mgs2vsoYY0yAGjduzN///nfuv/9+kpKSePnll2nXrh233XZbzuD1fv36sXfvXrp3787hw4dRVf72t7+d9Fq9e/emX79+OYPXI1WtWpUmTZqwevVq2rRpA0CTJk145pln6NKlC1lZWcTHxzNq1Cjq1q0byN9+pkRVQ1lw69atNSUlJZRlR9Vnj8Pit+GxbdGfjPm922Hnahi4zK4INMaYEmDNmjU0btw41AxpaWl069aNlStXhpojTHmtBxFZoqqtC3uunQo8U4lJkHkYfvw+uq+becQrs5B0rXWqjDHGmLOEdazOVPXskgtRHsC+aQFk/Gzjq4wxxgSqXr16Jfpo1ZmyjtWZiiy5EE3rZ0JcGahvZRaMMcaYs4V1rM5UhUQoVyX6Hat1M6BueyhTIbqva4wxxpiYsY7VmRLxxllFs+TC/s2w+xu7GtAYY4w5y1jHKhoSG0b3iNU6v8yCja8yxhhjzirWsYqG6g0gfTsc/ik6r7d+JlSpc3wuQmOMMaaYmjp1KqtXry70caNHj+bdd98t8DGpqal8+umn0Yp2WqxjFQ3ZA9ijcWVg5hH4bq53tMrKLBhjjCnGMjMzi9yx6tevH3fffXeBj3GhY2WV16Mh58rA9VCr1Zm91uaFXpkFG19ljDEl2vNfPc/avWuj+pqNqjXiT23+VOBjchcIHTFiBOnp6cyZM4e2bdsye/Zs9u/fz5gxY+jYsSPHjh3jT3/6E5999hkiwr333suAAQNYsmQJf/jDH0hPTycxMZFx48Zx/vnn06lTJ6688krmz59Ply5dmD59OnPnzuWZZ55hypQpzJo1izfffJOjR4/SoEED/vGPf3DOOecwdOhQEhISGDRoEJ06dTopS9u2bRkyZAiHDh1i3rx5PProozzxxBMsWLCAGjVqkJWVRcOGDVm0aBGJiYlRfV8jWccqGqrWA4mLzgD2dTP8MgtXnflrGWOMMVGUmZnJV199xaeffspTTz3FzJkzefPNN9m4cSNLly6ldOnS7N27l4yMDAYMGMC0adOoUaMGEydO5PHHH2fs2LEA7N+/n7lz5wKwbt06unXrRo8ePQCoUqUK9957LwBPPPEEY8aMYcCAAUXKMmzYMFJSUnjttdcAWLt2LePHj+f3v/89M2fOpGXLljHtVEERO1Yicj3wMhAHvK2qw3Pd3xt4EfjBv+k1VX07ijndVroMVKsfnY7V+plQ90ors2CMMSVcYUeWwnDLLbcA0KpVK9LS0gCYOXMm/fr1o3Rpr0tRrVo1Vq5cycqVK7n2Wu/sy7Fjxzj//PNzXid7wuW8rFy5kieeeIL9+/eTnp7OddddV+QsufXp04fu3bvz+9//nrFjx/Lb3/72lP7e01Fox0pE4oBRwLXAFmCxiExX1dwnRCeqav8YZDw7JDaEPWc4GfP+72HXWrj0ruhkMsYYY05R6dKlycrKyvn98OHDOT+XLVsWgLi4ODIzMwFQVSTXmGBVpWnTpidNupytQoX8Dx707t2bqVOn0rJlS8aNG8ecOXPyfFxeWXK78MILqVmzJrNmzeLLL79k/Pjx+S43WooyeL0NsF5Vv1PVo8AEoHtsY52FqjeAPRsg69jpv8Z6v8yCja8yxhgTkpo1a7Jz50727NnDkSNH+Pjjjwt8fJcuXRg9enRO52bv3r1ccskl7Nq1K6djlZGRwapVq/J8fsWKFTlw4EDO7wcOHOD8888nIyPjlDtCuV8LoG/fvvzmN7+hZ8+exMXFndLrnY6idKxqAZEzDG/xb8vtVhFZLiKTReTCqKQ7myQ2hGNHvOKep2vdTKhc5/hgeGOMMSZg8fHxDBkyhLZt29KtWzcaNWpU4OP79u1LnTp1aNGiBS1btuS9996jTJkyTJ48mT/96U+0bNmS5ORkFixYkOfzb7/9dl588UUuvfRSNmzYwNNPP03btm259tprC112bp07d2b16tUkJyczceJEAG666SbS09MDOQ0IIKpa8ANEbgOuU9W+/u93AW1UdUDEY6oD6ap6RET6AT1V9Rd5vNZ9wH0AderUabVp06bo/SVh27wIxl4Hd3wADbuc+vMzj8IL9aFFT+j2t+jnM8YY47w1a9bQuHHjsGMUKykpKTz88MP85z//KfJz8loPIrJEVVsX9tyiHLHaAkQegaoNbI18gKruUdUj/q9vAXnWHFDVN1W1taq2rlGjRhEWfRbJKblwmgPYNy+Eo+lWbd0YY4yJkuHDh3Prrbfyl7/8JbBlFqVjtRhIEpH6IlIGuB2YHvkAETk/4tebgDXRi3iWOKcanFP99IuErrcyC8YYY0w0DR48mE2bNtGhQ4fAllnoVYGqmiki/YHP8MotjFXVVSIyDEhR1enAQyJyE5AJ7AV6xzCzu6onnf6cgetmQp0roGxCdDMZY4wxJjBFqmOlqp8Cn+a6bUjEz48Cj0Y32lkoMQm+/ezUn/fjFti1Bi69M/qZjDHGGBMYmyswmhKT4OedcGjfqT1vnV9mwcZXGWOMMWc161hFU+Scgadi/UyofCHUuCT6mYwxxhgTGOtYRVN2x+pUBrBnHoXv5kCDX0KuyrXGGGNM0BISvLG+W7duzZm/LzU1lU8/PT4iaPr06QwfPjzP52eLfH5JYh2raKpSF0rFn1rJhewyC1Zt3RhjjEMuuOACJk+eDJzcsbrpppsYPHhwkZ9fkhRp8LoporjSUO2iU7sycP0MrzNmZRaMMcZE2P7ccxxZszaqr1m2cSPOe+yxIj02LS2Nbt268fXXXzNkyBAOHTrEvHnzePTRRzl06BApKSm89tprbNiwgTvvvJNjx47RtWtXRo4cSXp6es7zV65cyeHDh/nv//5vUlJSKF26NCNHjqRz586MGzcu53UAunXrxqBBg+jYsSO/+93vSElJQUTo06cPDz/8cFTfi1ixjlW0JZ5iyYV1M6HuFVC2YuwyGWOMMaepTJkyDBs27IQO0Lhx43LuHzhwIAMHDuTXv/41o0ePzvM1Ro0aBcCKFStYu3YtXbp04dtv8z+7k5qayg8//MDKlSsB2L9/f5T+mtizjlW0ZZdcOJbpHcEqSHaZheQ7gslmjDHmrFHUI0thW7hwIVOnTgXgjjvuYNCgQSc9Zt68eQwY4M2E16hRI+rWrVtgx+qiiy7iu+++Y8CAAdx444106XIaU8WFxMZYRVtiQ8jKgP1FmAcxu8yCja8yxhhTjOU3L3Hp0qXJysrK+f3w4cMAVK1alWXLltGpUydGjRpF3759A8kZDdaxirbqSd73ogxgXz8TKtWGGqc2e7cxxhgTpIoVK3LgwIE872vXrh1TpkwBYMKECXk+5qqrrmL8+PEAfPvtt2zevJlLLrmEevXqkZqaSlZWFt9//z1fffUVALt37yYrK4tbb72Vp59+mq+//joGf1VsWMcq2hIbeN8LG2eVXWYhycosGGOMcVvnzp1ZvXo1ycnJTJw48YT7XnrpJUaOHEmbNm3Ytm0blStXPun5DzzwAMeOHaN58+b06tWLcePGUbZsWdq3b0/9+vVp3rw5gwYN4rLLLgPghx9+oFOnTiQnJ9O7d+9AJ1E+UzbGKtrKV4UKNQo/YvX9Iq/MglVbN8YY45D09HQA6tWrlzN4vFq1aixevPiEx/Xu3RuAWrVqsWjRIkSECRMm0Lp165OeX65cuRMGvGcTkZwjWbmdTUepIlnHKhYSGxZ+xGqdX2bhoquDyWSMMcbEwJIlS+jfvz+qSpUqVRg7dmzYkUJlHatYSEyCNf9b8GPWz4Q67azMgjHGmLNax44dWbZsWdgxnGFjrGKhehIc3AMH9+Z9/49bYOdquxrQGGOMKWasYxULOZMx53M6cP1M77uNrzLGGGOKFetYxULOlYH5DGBfNwMq1YJzGweXyRhjjDExZx2rWKhSF+LK5N2xyjwK382FBlZmwRhjjClurGMVC6XioHoD2LP+5Pu+/xKOHrDxVcYYY0qU55577rSe17dvX1avXl3gY0aPHs277757Wq8fbXZVYKxUb+ANUM9t/QwoVRrqW5kFY4wxJcdzzz3HY3nMf6iqqCqlSuV9rOftt98u9LX79et3xvmixTpWsZLYEL75FI5lQFz88dvXzYQ6V0C5SuFlM8YY47z/TPqW3d+nR/U1Ey9MoGPPhgU+5t1332XEiBGICC1atOCZZ56hT58+7Nq1ixo1avDOO+9Qp04devfuTaVKlUhJSWH79u288MIL9OjRg23bttGrVy9++uknMjMzeeONN/jkk084dOgQycnJNG3alGeffZauXbvSuXPnnEmchw8fzuLFizl06BA9evTgqaeeAqBTp06MGDGC1q1bk5CQwMCBA/n4448pX74806ZNo2bNmgwdOpSEhAQGDRpEp06daNu2LbNnz2b//v2MGTOGjh07cvDgQXr37s3atWtp3LgxaWlpjBo1KqegabTYqcBYSUyCrEzYu/H4bT/+ADtXeeOrjDHGGMesWrWKZ599llmzZrFs2TJefvll+vfvz913383y5cu58847eeihh3Iev23bNubNm8fHH3/M4MGDAXjvvfe47rrrSE1NZdmyZSQnJzN8+HDKly9PampqTqX1b775hrvvvpulS5dSt25dnn32WVJSUli+fDlz585l+fLlJ+X7+eefadeuHcuWLeOqq67irbfeyvPvyMzM5KuvvuKll17K6aC9/vrrVK1aleXLl/Pkk0+yZMmSaL99gB2xip3EiMmYa/ifDrLLLNj4KmOMMYUo7MhSLMyaNYsePXqQmJgIeFPZLFy4kA8//BCAu+66iz/+8Y85j7/55pspVaoUTZo0YceOHQBcfvnl9OnTh4yMDG6++WaSk5PzXFbdunVp165dzu+TJk3izTffJDMzk23btrF69WpatGhxwnPKlClDt27dAGjVqhUzZszI87VvueWWnMekpaUBMG/ePAYOHAhAs2bNTnrtaLEjVrFS3e9Y7YmoZbV+BlS8AM5tEk4mY4wxpgCqihRyxXrk/WXLlj3huQBXXXUV//73v6lVqxZ33XVXvoPKK1SokPPzxo0bGTFiBF988QXLly/nxhtv5PDhwyc9Jz4+Pmf5cXFxZGZm5vna2bkiH5OdL9asYxUr5SpBwnnHi4Qey/DKLCRZmQVjjDFuuuaaa5g0aRJ79uwBYO/evVx55ZVMmDABgPHjx9OhQ4cCX2PTpk2ce+653Hvvvfzud7/LmUw5Pj6ejIyMPJ/z008/UaFCBSpXrsyOHTv417/+FcW/ytOhQwcmTZoEwOrVq1mxYkXUlwF2KjC2EpOOd6y+/xKO/GTV1o0xxjiradOmPP7441x99dXExcVx6aWX8sorr9CnTx9efPHFnMHrBZkzZw4vvvgi8fHxJCQk5Byxuu+++2jRogWXXXYZzz777AnPadmyJZdeeilNmzbloosuon379lH/2x544AHuueceWrRowaWXXkqLFi2oXLly1JcjQdwR2GIAACAASURBVB0ay61169aakpISyrID8/HDsPJD+FMazBwKC1+DP260KwKNMcbkac2aNTRubLNyxMKxY8fIyMigXLlybNiwgWuuuYZvv/2WMmXKnPTYvNaDiCxR1UIvIbQjVrGU2BAO74efd3sD163MgjHGGBOKgwcP0rlzZzIyMlBV3njjjTw7VWfKOlaxlH1l4Ma5sGMl/PKpcPMYY4wxJVTFihUJ4kyZDV6PpewrA78c7X23MgvGGGNMsWYdq1iqfCGULgdbFluZBWOMMaYEsI5VLJUq5c0ZCFZmwRhjjCkBrGMVa9njrKzMgjHGGFPsWccq1s5vCfHnwEVXh53EGGOMKdSVV15Z6GNeeuklDh48GNMcqampfPrppzm/T58+neHDh8d0mdFgHatYa/cAPPgVlIt+ETJjjDEm2hYsWFDoY06nY3Xs2LFTenzujtVNN92UM9Gzy6zcQqyVLgtVLgw7hTHGmLPM7HFvsnPTd1F9zXPrXkTn3vcV+JiEhATS09OZM2cOQ4cOJTExkZUrV9KqVSv++c9/8uqrr7J161Y6d+5MYmIis2fP5vPPP+fPf/4zR44c4eKLL+add94hISGBevXq0adPHz7//HP69+/P5ZdfzoMPPsiuXbs455xzeOutt2jUqBEffPABTz31FHFxcVSuXJmZM2cyZMgQDh06xLx583j00Uc5dOgQKSkpvPbaa/Tu3ZtKlSqRkpLC9u3beeGFF+jRowdZWVn079+fuXPnUr9+fbKysujTpw89evSI6vtYEOtYGWOMMSZPS5cuZdWqVVxwwQW0b9+e+fPn89BDDzFy5Ehmz55NYmIiu3fv5plnnmHmzJlUqFCB559/npEjRzJkyBAAypUrx7x58wBvLsLRo0eTlJTEl19+yQMPPMCsWbMYNmwYn332GbVq1WL//v2UKVOGYcOG5XSkAMaNG3dCtm3btjFv3jzWrl3LTTfdRI8ePfjwww9JS0tjxYoV7Ny5k8aNG9OnT59A3zPrWBljjDEOKuzIUhDatGlD7dq1AUhOTiYtLe2kSZgXLVrE6tWrc+b3O3r0KFdccUXO/b169QIgPT2dBQsWcNttt+Xcd+TIEQDat29P79696dmzJ7fcckuRst18882UKlWKJk2asGPHDgDmzZvHbbfdRqlSpTjvvPPo3Lnzaf7lp886VsYYY4zJU9myZXN+jouLIzMz86THqCrXXnst77//fp6vUaFCBQCysrKoUqUKqampJz1m9OjRfPnll3zyySckJyfn+ZiCsmXPexzW/MeRbPC6McYYY05JxYoVOXDgAADt2rVj/vz5rF+/HvDm5Pv2229Pek6lSpWoX78+H3zwAeB1gpYtWwbAhg0baNu2LcOGDSMxMZHvv//+hGUUVYcOHZgyZQpZWVns2LGDOXPmnMFfeXqsY2WMMcaYU3LffffRtWtXOnfuTI0aNRg3bhy//vWvadGiBe3atWPt2rV5Pm/8+PGMGTOGli1b0rRpU6ZNmwbAI488QvPmzWnWrBlXXXUVLVu2pHPnzqxevZrk5GQmTpxYpFy33nortWvXplmzZtx///20bduWypWDvSpfwjps1rp1aw1iMkRjjDHmbLFmzRoaN24cdoyzWnp6OgkJCezZs4c2bdowf/58zjvvvFN6jbzWg4gsUdXWhT3XxlgZY4wxptjo1q0b+/fv5+jRozz55JOn3Kk6U9axMsYYY0yxEca4qkg2xsoYY4xxiAtXtpVkZ/r+W8fKGGOMcUS5cuXYs2ePda5Coqrs2bOHcuXKnfZr2KlAY4wxxhG1a9dmy5Yt7Nq1K+woJVa5cuVyiqKeDutYGWOMMY6Ij4+nfv36YccwZ8BOBRpjjDHGRIl1rIwxxhhjosQ6VsYYY4wxURJa5XUR2QVsivFiEoHdMV5GUViOE1mOE7mQw4UMYDlysxxuZQDLkVtJylFXVWsU9qDQOlZBEJGUopSftxyWo6TncCGD5bAcrmewHJajKOxUoDHGGGNMlFjHyhhjjDEmSop7x+rNsAP4LMeJLMeJXMjhQgawHLlZjuNcyACWIzfLkUuxHmNljDHGGBOk4n7EyhhjjDEmMNaxMsYYY4yJEutYGWOMMcZEiXWsjDHGGHNKRKSaiFQNO4eLrGMVZSJSSkT6iMgnIrJMRJaIyAQR6RRwjgQRGSYiq0TkRxHZJSKLRKR3wDmuj/i5soiMEZHlIvKeiNQMMkteRORfYWcIm4hUEpFWLm0kg1wvfrscLiJrRWSP/7XGv61KUDlyZaoqIhVDWnbpiJ8TRKS1iFQLI0vYXGwbuQX8v1LH35/tAr4EFovITv+2ekHl8LPUEJFLRaS5iCQEuezCWMcq+sYAdYC/ALOBT/zbnhCRAQHmGA98B1wHPAW8AtwFdBaR5wLMEbmsvwLbgF8Bi4H/CSKAiFyWz1crIDmIDH6OvSLytohcIyIS1HLzyPFPEUn0f74OWAU8D6SKyG0B5nBivQCTgH1AJ1WtrqrVgc7+bR8EFUJELhCRd0XkR7ypOVaJyGYRGSoi8QFl6A3sEJFvRaQrsByvbSwTkV8HkaEwIrIiwMW50jZc+V+ZCHwEnKeqSaraADgfmApMCCKAiDQRkZnAQrzO3dvAChEZJyKVg8hQmGJVbkFE+qjqWP/n2sDfgVbAaqC3qn4bQIblqtoi4vdFqtpORMoCqaraONYZ/OUuU9WWEb8vVtXLRaQUsFpVGwWU42tVvcz/OVVVkyPuO+H3GGY4BswF8urMtFPV8rHO4Of4BngV+DVQD5gMvK+qi4JYfkSOFara3P95AXCHqqb5na0vIttNjHM4s15U9ZJTvS8GOWYBw1R1jojcAnQEngAeBc5V1fsCyLACr+NQEVgGXKqqG/yjyzMit20xznFLfncBo4syX1uUcrjSNlz5X1mnqkmnel+UMywC7lHVb0SkDfCgqt4jIvcC16lqj1hnKEzpwh9yVukPjPV/Hon3aeNaoDvwBnBNABkyRORif2N0GXAUQFWPiEiQvdifRaSDqs4TkV8Be/0cWQEfLTlXRP6At0GoJCKix3vzQR0xXQPcr6rrct8hIt8HlAHgZ1V9DXhNROoAtwOv+6cUJqjqYwHlKCUilVT1JyAL2AygqrsjTwMFwJX1sklE/gj8XVV3+MuvCfQGgsxRXVXnAKjqhyLyuKr+jHe0e21AGY6p6m5gt4ikq+oGP8+OgA+yTsQ76p7XNrNcgDlcaRuu/K8sEZHX8Q5aZC/3QuAeYGlAGcqr6jcAqvqViIz2f35LRB4OKEOBilvHKlJDVe3p//yRiAwJaLmPALNF5DAQj7fzRERqAB8HlAGgH/C2iDQEVgJ9InKMCjDHW3iffsH7Z0wEdonIeUBqQBmGkn8nLsjTszl7JlXdDLwAvCAil+C3k4A8hddGRwHzgQ9EZBrwC+D/AswxFDfWSy9gMDBXRM71b9sBTAd65vus6NslIr8BZgG3AmkA/gehoD6EbBaRv+D9z64Vkb8CHwK/xDuNH5TlwAhVXZn7DhH5ZYA5XGkbQ3Hjf+Vu4Hd425BaeNu0LXjvx5iAMmwQkSeBL4Bb8Pcj/ulyJ/o0xe1U4E6887yC94bXU9UM/76VqtosoByC9+lzdxDLM2cHERmpqn8IOweAiDQA7gUa4m2MtgBTVfWzUIOVYP5RzBFAE7ydxSOquk1EquON8ZkSQIZKwIN4R4pewxuj+VtgE/CMqgbSuRKRjsAm/wNI7vtaq2pKEDmMe/wj/I/h/Z8sA4ar6gF/fFXjoIdW5KW4dazuyXXTdFXd5x8deSioUy3+xqlG9mH0iNtbqOryIDIURER+q6rvBLi8Rnifbr5U1fSI269X1SCPkJwk6PfCFE2Q68X/IHQbXmdiMt6Ru+7AWrzxPFlB5DDucaVtOJTjHLwhN4o3XrQX3tHVtXjjA9MLeHqJUaw6Vi4QkZ7AS8BOvFOBvVV1sX9fzkDuMInIZlWtE9CyHsL7BLwG7+qVgao6zb8v9PcjyPeikBxDVHWY5cjJEWQbfR04FygD/ASUBf4XuAHYoaoDg8jhZ7kOqA3MVNVNEbfnXJgT4+XHAX39DP+nqvMj7ntCVZ+JdQZ/WaXxTjn9F3AB3o58KzANGJN9JiKAHE60DYdyTMIbW1UeuARvuz4J70rv81T1rgAyRLbRf6nqgoj7AmujBSlWHauIf8ab8Y6QBP7PKCKpQFf/EH4b4F3gMX8w6lJVvTTWGfwc+R0ZE7zxZ2UDyrECuEJV08WrczIZ+IeqvhzU++HKe1EQhzp4QXZonFgv4l8l6Y/R2A6cr6pH/e3J0uwrKAPI8RzQAfgab0f1kqq+6t8XyIcQEXkbOAf4Cq88y9zs09dBfhASkfeB/XjjMrf4N9fGGyRdTVV7BZTDlbbhSo5UVU32j6Bt83Oo//uyIK4adaWNFsSJgV5R9A+8f8anOPmf8Z94hy1jLS57HIJ/xUJn4GPxyj8E2YutiTc+Yl+u2wVYcPLDYyYu+/Cwepf0dwImi0hd8r50OBaceC9E5Kf87sL7BFiicuDIegEyAVQ1Q7yyJNlX8maKd5l7UH6FV94gU0SGAu+JyEWq+jDB/a+0yd45ishreFetfohXIiTIywIvy6OUwRZgkYjEvGxOBFfahis58JerIvJp9hXe/u9B7d9caaP5Km4dKxf+GQ+IX24BwD9y1RmvqFrTgDKAdwVigqqedOWdiMwJMMd2EUnOzuEfueqGVxYjkE9ZuPNe7Acuz75sO1eOIC+ZdiWHK+tlu4gkqGq6qkbOFHAefrmUgJRW1ewd6H7xyqS8KSIf4J0CCkLOcvws94l3RfUsIMjq1vvEK1Y7JXv8kHg1+G7j5I54LLnSNlzJkRKRo09EjouBAwFlcKWN5k9Vi80XsAjvH69UxG2l8I5UfRlQhpZAgzxujwfuDPs9CmGd1MY7957Xfe3Dzhfwe/EM3qetvO57vqTlcP0LqIBXmDOo5X0MXJ3P+soKKMM/gevzuL0vkBHge1EPr5bVLuBb/2uXf1v9ktY2XM/hZ5GAluNEGy3oq7iNsaqHN/3CLzj+qaYqXk92sKpuDClXN1UNsoZVfjnuU9U3LYcbGczJXFkvIjJUVYcGvMzyAKp6KI/7aqnqD0HmcYV45SZEHSlfE0bbcDzHmxrArABnk2I1V6CqpqlqL/WmO7gCuFJVa/i3hdKp8oV+lZWvX9gBfC7kcCED/lia0LmSA0fWC3BT0AtU1UO5O1XZ6yXMTpWIhNrRVdU96s0KEHqH2xd428iHKzlahx3AobYBFLOOVSTH/hmdGFCH5YjkQgZwZ+PoSg5X1osrOVxYL6HvOH2u5HClbbiSY2fYAXCnbQDFuGMVwYU3/P6wA/h+FXYAnws5XMgA7mwcXcnhynppFXYAnwvrxYUdJ7iTw5W24UQOjRhMHyJX2gZQMjpWgb/hItJGRC73f24CdBCRG0LI0UhErhGRBABV3eLfHug/Qtg5xNNTRG7zf74G+KOIPOBfaRQmJzaOhJQj7LaRHz1+JVpQc4zmJ/T24ciO06UcTrQNV3JkC/PskCttI1vYO5WYC/oNF5E/A68Ab4g3melreJeADhaRxwPM8RBeYdQBwEoR6R5x93MlLMcovAlT78KrddYPSAGuAv4WUAbAq6wtIr/zL7SI3Dj2Keh5xTGHI22jMH3DXLhLO88gd5wiEici94vI0yLSPtd9TwSVoxChto0IgeUQkWr5fFXHqwIfKkeG/hS7qwLPA/4MZAFD8DbYt+DNYzRQA5hAVLxK48l4Uw5sB2qr6k/+FT9fagCVaSNyhFrx3JUc4k7V4tArazuWI/S24ecosGCqqoZe708CqogvItXyuwuvsnbtWGfwczhRXduVtuFQjmN4E3JHnqJW//daqhrzemuutNGChL7BiLJxwCd4tT1mA+OBbniTVY72v8dapqoeAw6KyAZV/Qm8K35EJMjJXF2oeO5KDleqFrtQWdulHC60DXCkYGphO8+AYuwi/x3nuQFlAHeqazvRNhzK8R1wjapuDjGHK200X8XtVGBNVX1VVYcDVVT1eVXd7H8arxtQhqPizQAOEeMjRKQy3pG0oGwXkeTsX/wdWDcgkeAqnruSY3vEGB5nKmvjdXAqSbCVtV3K4ULbAG8+z/y2D+8FmGM/kKSqlXJ9VcSbly0I3wGdVLV+xNdFqlofOGmnHkMnVNf26ySlEnx1bVfahis5XsKrDZmXFwLK4EobzV9B1UPPti+8w4DZPz+T677lAWUom8/tiUDzAN8LJyqeu5Ijn+WXuMrajuVwtm2E8YUDFfGBB4GW+dw3IMD3wvnq2vYVzpcrbbSgr+I2xmoY8IL6pxcibm8ADFfVHgHlEKANUAvvEOVW4CsN+M22HCdkqAxcnyvDZ+odsQkqgxOVtV3J4S8v9Lbh5wi9fRg3udI2HMrRCG9YTWSO6aq6JsgcLitWHSsXiEgX4HVgHZC9g6oNNAAeUNXPLUewOUTkbryLGj7PleFa4ClVfTfWGSKyuLJxDD2HC23Dz2Ht48QMTuw4XcjhSttwKMef8Ma5TQC2ROS4HZig3jCcIHKE3jYKUuw6ViJyHXAzJ77h01T1/wJa/hqgq6qm5bq9PvCpqja2HMHmEJFvgLa5d04iUhXvSs2Gsc7gL8+VjaMrOUJvG/7yrH0cz+DKjtOVHK60DVdyfAs0VdWMXLeXAVapalIAGZxoGwUpVlcFishLQEO8gX6Rb/hDItJVVQcGEKN0xLIj/QDEB7B8y3Eywetk55ZFsFcYPQ60ym/jiNduS1IOF9oGWPuI9Dvy3nGOBFYBQe20XMnhSttwJUcWcAHeVXmRzie4i7NcaRv5KlYdK+CGvHruIjIR+BYIomM1FlgsIhOA7MtP6wC9gDEBLN9ynOxZ4GsR+TxXhmuBpwPKAO5sHF3J4ULbAGsfuZcV9o7TpRyutA1Xcvwe+EJE1uXK0QDoH1AGV9pGvorVqUARWQ70VdWvct3eBhijwRWCbMzx87+C96l8uqquDmL5liPPDFWB63Jl+ExV9wWY4R68wrV5bhxVdVxJyuFnCb1t+DmsfXgZrsebLSLPHWeAQyqcyOFnCb1tOJajFMcvOMnOsVi9+o1BLN+ZtpGf4taxugx4A6jI8VMMFwI/4Q2GXRJWLlX9OoxlW458M3RT1Y9DWK4rG0cncuSRK/S24ecose0j7B2naznyyBVK23A4x32qGuhUMq62jWzFqmOVzS/8mPOGq+r2kPMENgWD5Th7Mvg5XNk4upLDlfXiSo7Q10sYO07Hc7jSNizH8QxOtI1sxa3yOgCqul1Vl6hqCt6Eu2ELctxKQSzHcS5kABgWdgCfKzlcWS+u5HBhvbiwDQV3crjSNizHca60DaCYdqxyuSnsAMBTYQfwWY7j7g87gM+FjRK4k8OFtgHWPiK5kAHcyeFK23Alx6/CDoA7bQMoGR2rwN9wEaksIr1E5A8i8jBQVkSqhJDjPP+0KCJSAyglIk2DzpGHNmEu3K+TVNsvMhc2VzaOoeQQkatE5BL/5w5AAxG5MYQcCSLSQ0QeFpEBQDV/HEfYXGgfLuw4IaQcItJGRC73f24CdBCRG8LIkktQV+Hly/+f7ekX+w2TK20UKKZjrCKJSClVDewSTBeK/Pk57gcG43Usnwd649X4aI837U8gl7OLyCu5bwLuwq/Jo6oPBZBhqqre7P/cHW8i0TnAlcBfArwarw6wU1UPi4jgrZPLgNXAW+pPjBxAjpuAz1X1cBDLKyDHS3id7NLAZ8A1wL+Aq4GlqvpIQDl6Ao8Ay4DOwAK8D53NgTtVdUUQOfwszlaUFpHfquo7AS6vEd778KVGTFMmItcHeHXin4GueG10BtAWb9vxS7yLCp4NKMf03DfhtdVZAKoayJkZEflKVdv4P9+LN2/fR0AX4H+DKM4pIm2BNar6k3jTcw3m+Hb0OVX9MdYZClPsOlYi0hm4Fe9qwEy8SzLfVtX1AS3flQq5K/A2AuXx6n00UNXtfo7ZqpocUI4teBuizzl+9HAEMAhAVf8eQIalqnqp//MCvJ3lRhFJBL5Q1ZaxzuAveyXeJLsHReR54GJgKvALAFXtE1COQ8DPeJ2Y9/F2EIFfTSMiq4BmeG30B6CW/97E43WsmgWUYznQzl92IjBeVa8TkRbAaFW9MqAcTleUFpHNqlonoGU9hLfTXgMkAwNVdZp/X2CDpf3taDJQFtgO1I7YoX+pqi0CyvE1XsfhbbwOt+D9794OoKpzA8oRuS1djFc7cpeIVAAWBVHSyN9utFTVTBF5EzgITMb7YNZSVW+JdYbCFKsCoSIyHKgJfAGcB2wENgAfiMhzqvpBEDEIv8gfeDPAHwQOisiG7CsjVXWfiATZm26MV8DueuARVf1BRP4cRIcqQuTfW1pVNwKo6m4RCbKgXCl/nYD3ifdy/2jqP0VkWYA51uJ15noA/w94R0Q+At4PagPtU1XViHWQvZ6yCHaYggDZE1L/DJzrh1suIpUCzBF6RWm/k5nnXXjb1qDci1eFPl1E6gGTRaSeqr5MsNvRTP9DR/Z29CfwJjAPeNvRGq/A9eN429FUETkU8P8reMNJquL9f4qq7gJQ1Z9FJJAj7njb0exltY7oZM8TkdSAMhSoWHWsgBuze8ziVXOeq6qPiMhk4D9AEB0rVyrkZolIvL+RzhmzIiLlCHCnpaoHgN+LSCu8DsQnQS7f11JEfsLbIJcVkfP8o3dlgLgAc3wvIr9Q1VlAGt5R1U0iUj3ADOB1aPYBbwFv+ePwegLDRaS2ql4YUI5PROQ/QDm8T+KTRGQR3qnAfweUAeBT4P9EZC7eaZ8PAESkGsHuxF2oKF0Tr45W7rpZgneKNChx2af/VDVNRDrhda7qEuw6OSoi5/gfiFpl3yjeZNmBdaz8D2B/E5EP/O87CGf/XRlYgn8AIWJbmkBw62VlxGnpZSLSWlVTRKQhkFHYk4NQrE4F+p/6O6vqXn88yyRVbefft0pVAxm4LW4U+asDbMvj028toLGqzgwqS8SyBXgAuEJVfxP08vPIUwXvvVgY0PIuxBtbFgf8CHQAlgJVgUGq+kVAOXIO5+dxX11Vzb1jj2WWK/A6eotE5GLgv4DNwOSAx0beADQBlqnqDP+2UkC8qh4JKEPoFaVFZAzwjqrOy+O+91T1jlhn8Jc1C/iDqqZG3FYabxqkO1U1kA9EIlI2r/XvnzI+P8jxd7mWfyPQXlUfC2P5uYnIOUDN7LMBMV5WZeBloCOwG2981ff+10OqGuTR/zwVt45VL+AF4BugEfDfqvqJeFfEvRzURsHPUpOIAaiquiOoZVsOdzP4ORrjTRaePQnx4oA7EZ1UdU5QyyuMQ+sl9BzieEXpoIhIbbzTcCcVdxaR9qo6P4RYuXMkRA6qtxzB5hCRisBF+NvRsLYbeSlWHSvIOXx/EbA+9wDygJafDIzGO2S6BW/jWBvYjzetTiDTdeTKEXl14n68DudSB3IE8n6IyKV4Ux2F+l5E5Al9B+5CDhfahks5CuLCztOFDI7lCGwwv+UocgYn2kZxG2OFfxrwIqCzP5hunaquDTDCOOB+Vf0y8kYRaQe8AwRyBVohOcY5kiOo9+OdAjKMCyhDvjtwEXGiIxF0DtxoGy7lKMhqvNOCJT0DBJhDRP6Q311AQhAZLMcpcaKNFquOlYhcDfwV75NmK2A+UFVEMoC7VPX7gp4fJRVyb6AB/DEkFQJYvuVwMwO4swN3JYcr68WJHC7stFzI4FIO4DngRbzSPbkFeRGO5fA51DbyVaw6VniFH7uoV1ejPjBSVduLyLXAGLwiZrH2L//Kt3c5PgD1QuBuIJCidpbDyQzgyA7coRyurBdXcoS+03Ikg0s5vgamquqS3HeISF/LEUoOV9pGvorVGCsRWa5+wTYRicMb9HmZ/3uQVwV25Xj15OwBqNNV9dMglm85nM3wCl5R0Lx24BtVNZApKlzJ4WcJfb24kkO84rUD8tlpfR9EGQwXMjiW4xJgr/r1mnLdVzOocYmW44TlONE2ClLcOlZj8QbifoG3kfxBVf/gXwr6taq6MDecKcFc2IG7lMMc58hOK/QMLuUw7jkb2kZx61jF41XsbYI379dYVT0m3vQD52oA9Xn8GhuP4u20zvVv3glMA4YHdaWi5XArgzmZK+vFlRzGPRFt42aghn9zmG3UcpwFnDgfGS2qmqGqr6tqf1V9K7v2i6oeCqJT5ZuEV7W4s6pWV9XqeJNl7ieYyu+Ww80MiEhlERkuImtEZI//tca/rUpJy4Ej68WVHBHrZW1Y68WFDC7l4Hjb6JSrbewjnDZa4nM41DbyVdyOWCUAfwRuwRszchRvrsDRqjouoAzfqOolp3qf5SjeGfxlfYY3G/3f1S98KN50Mr2Ba1T12hKWw5X14kqO/NbLPcAvg1gvLmRwLIcrbcNyHF+OE22jIMXqiBUwHvgOb8Lfp4BXgLvwalo9F1CGTSLyR/GKLwLeeV/xZq4PotyD5XAzA0A9VX1eI6pJq+p2VR1OsLVXXMnhynpxJUd+6+V5glsvLmRwKYcrbcNyHOdK28hXcetY1VPVcaq6RVVHAjep6jrgt3hHsYLQC6gOzBWRfSKyF5gDVMOb6DYolsOtDODGRsmlHK6sF1dyuLBeXMjgUg5X2oblOM6VtpE/VS02X3izr3fwf/4V3sTH2fd9E2CORsAvgYRct18f8PthOdzKUBV4HliLNyZhL7DGv61aScvhynpxJYcL68WFDC7lcKVtWA4320a+GcMOEOU3vAXwFd6g03lAQ//2GnizXgeR4SG8SaCnAmlA94j7vg7wvbAcDmWIWJ5tHB1bL67kcGi9hJ7BlRyutA3L4V7bKDBf2AECXBG/DWg5K7JXNlAPSAEG+r8vDfDvtRwOZfCX5cpGyZUcrqwXV3KEvl5cyOBYDlfahuVwrG0U9FXcprQpyFN486DFWpz6s2urapqIb0K9IAAAATFJREFUdAImi0hdvEKMQbEcbmUAr8ZaK1VNF5F6foZ6qvpyCc3hynpxJYcL68WFDC7lcKVtWI7jXGkb+SpWg9dFZHk+XyuAmoW+QHRsF5Hk7F/8RtgNSASaB5TBcriXAXJtlIBOQFcRGUmIG8cQc7iyXlzJ4cJ6cSGDSzlcaRuW4zhX2kb+wj5kFs0vYAeQDNTN9VUP2BpQhtrAefnc1z7A98JyOJTBX9YsIDnXbaXx5uw7VgJzuLJeXMkR+npxIYNjOVxpG5bDsbZR0FdxKxA6BnhHVeflcd97qnpHCLGMAUBEagOZGlF/JeK+9qo6vyTlMCdyYb24kMGlHMY9Z0PbKFYdK2OMMcaYMBWrMVbGGGOMMWGyjpUxxhhjTJRYx8oYY4wxJkqsY2WMMcYYEyX/H3c06GxoHHUPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb287b2240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cosine_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_cosine_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_tfidf_ten_ks.items()}\n",
    "\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [cosine_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Cosine Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Alpha Factors\n",
    "Just like we did in project 4, let's evaluate the alpha factors. For this section, we'll just be looking at the cosine similarities, but it can be applied to the jaccard similarities as well.\n",
    "### Price Data\n",
    "Let's get yearly pricing to run the factor against, since 10-Ks are produced annually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AA</th>\n",
       "      <th>AAAP</th>\n",
       "      <th>AABA</th>\n",
       "      <th>AAC</th>\n",
       "      <th>AADR</th>\n",
       "      <th>AAIT</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAMC</th>\n",
       "      <th>AAME</th>\n",
       "      <th>...</th>\n",
       "      <th>ZUMZ</th>\n",
       "      <th>ZUO</th>\n",
       "      <th>ZVZZC</th>\n",
       "      <th>ZVZZCNX</th>\n",
       "      <th>ZX</th>\n",
       "      <th>ZXYZ_A</th>\n",
       "      <th>ZYME</th>\n",
       "      <th>ZYNE</th>\n",
       "      <th>ZZK</th>\n",
       "      <th>ZZZ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>6.40835399</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>10.61223421</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>9.04493392</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.80659044</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>3.14982608</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.32975302</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.39785181</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.69892591</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.51433946</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.63082711</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.09677772</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.15548749</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.70833333</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.85161770</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.32812500</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.71821581</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>29.61250000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.54301839</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>52.37855258</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>108.17500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.15548749</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>37.09385272</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.03000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.86380242</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>19.31590395</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.87000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.05391026</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>12.16813872</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.17500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.51899897</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>19.81048865</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>22.51500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.79570362</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>16.32807033</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>37.68000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.88889374</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>22.55441748</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>39.18000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>35.76072005</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.51613326</td>\n",
       "      <td>...</td>\n",
       "      <td>21.61000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>23.61133822</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>25.54000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>51.85015549</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.75842757</td>\n",
       "      <td>...</td>\n",
       "      <td>29.54000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>24.89183834</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>23.26000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>14.16371007</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.30466169</td>\n",
       "      <td>...</td>\n",
       "      <td>24.36000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>10.58953275</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>12.20000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>7.44292854</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.69147070</td>\n",
       "      <td>...</td>\n",
       "      <td>7.45000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>21.05033797</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.78000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.66025539</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.19283355</td>\n",
       "      <td>...</td>\n",
       "      <td>12.72000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>28.06937567</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.63000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>28.82785959</td>\n",
       "      <td>nan</td>\n",
       "      <td>9.63825546</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.89175945</td>\n",
       "      <td>...</td>\n",
       "      <td>26.87000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>23.66553928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.13000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>27.36247977</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.88171380</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.85276561</td>\n",
       "      <td>...</td>\n",
       "      <td>27.76000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.99225131</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>28.01179940</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>19.90000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>30.02536396</td>\n",
       "      <td>27.10695167</td>\n",
       "      <td>12.99864622</td>\n",
       "      <td>82.00000000</td>\n",
       "      <td>2.97706591</td>\n",
       "      <td>...</td>\n",
       "      <td>19.41000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.99000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>39.53485221</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>40.44000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>36.74348283</td>\n",
       "      <td>31.27057728</td>\n",
       "      <td>24.31228275</td>\n",
       "      <td>930.00000000</td>\n",
       "      <td>3.96461868</td>\n",
       "      <td>...</td>\n",
       "      <td>26.00000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.60000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>39.43238724</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>50.51000000</td>\n",
       "      <td>30.92000000</td>\n",
       "      <td>36.88899069</td>\n",
       "      <td>32.90504993</td>\n",
       "      <td>51.89970750</td>\n",
       "      <td>310.12000000</td>\n",
       "      <td>3.94778326</td>\n",
       "      <td>...</td>\n",
       "      <td>38.63000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.43000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>0.50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>40.79862571</td>\n",
       "      <td>nan</td>\n",
       "      <td>31.27000000</td>\n",
       "      <td>33.26000000</td>\n",
       "      <td>19.06000000</td>\n",
       "      <td>38.06921608</td>\n",
       "      <td>30.53000000</td>\n",
       "      <td>41.33893271</td>\n",
       "      <td>17.16000000</td>\n",
       "      <td>4.91270038</td>\n",
       "      <td>...</td>\n",
       "      <td>15.12000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.79000000</td>\n",
       "      <td>14.10000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>10.07000000</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>1.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>44.93909238</td>\n",
       "      <td>28.08000000</td>\n",
       "      <td>26.76000000</td>\n",
       "      <td>38.67000000</td>\n",
       "      <td>7.24000000</td>\n",
       "      <td>39.81959334</td>\n",
       "      <td>nan</td>\n",
       "      <td>46.08991196</td>\n",
       "      <td>53.50000000</td>\n",
       "      <td>4.05320152</td>\n",
       "      <td>...</td>\n",
       "      <td>21.85000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.01000000</td>\n",
       "      <td>14.26000000</td>\n",
       "      <td>1.19890000</td>\n",
       "      <td>8.00000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.59000000</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>25.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>66.65391782</td>\n",
       "      <td>53.87000000</td>\n",
       "      <td>81.62000000</td>\n",
       "      <td>69.85000000</td>\n",
       "      <td>9.00000000</td>\n",
       "      <td>58.83570736</td>\n",
       "      <td>nan</td>\n",
       "      <td>51.80358470</td>\n",
       "      <td>81.60000000</td>\n",
       "      <td>3.37888199</td>\n",
       "      <td>...</td>\n",
       "      <td>20.82500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.10000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.21650000</td>\n",
       "      <td>nan</td>\n",
       "      <td>7.59310000</td>\n",
       "      <td>12.52000000</td>\n",
       "      <td>25.00000000</td>\n",
       "      <td>0.01000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>61.80000000</td>\n",
       "      <td>46.88000000</td>\n",
       "      <td>81.63000000</td>\n",
       "      <td>73.35000000</td>\n",
       "      <td>9.81000000</td>\n",
       "      <td>52.88000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>37.99000000</td>\n",
       "      <td>67.90000000</td>\n",
       "      <td>2.57500000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.30000000</td>\n",
       "      <td>26.74000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.38000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.34000000</td>\n",
       "      <td>9.17000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57 rows × 11941 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker               A          AA        AAAP         AABA         AAC  \\\n",
       "date                                                                      \n",
       "1962-01-01         nan         nan         nan          nan         nan   \n",
       "1963-01-01         nan         nan         nan          nan         nan   \n",
       "1964-01-01         nan         nan         nan          nan         nan   \n",
       "1965-01-01         nan         nan         nan          nan         nan   \n",
       "1966-01-01         nan         nan         nan          nan         nan   \n",
       "1967-01-01         nan         nan         nan          nan         nan   \n",
       "1968-01-01         nan         nan         nan          nan         nan   \n",
       "1969-01-01         nan         nan         nan          nan         nan   \n",
       "1970-01-01         nan         nan         nan          nan         nan   \n",
       "1971-01-01         nan         nan         nan          nan         nan   \n",
       "1972-01-01         nan         nan         nan          nan         nan   \n",
       "1973-01-01         nan         nan         nan          nan         nan   \n",
       "1974-01-01         nan         nan         nan          nan         nan   \n",
       "1975-01-01         nan         nan         nan          nan         nan   \n",
       "1976-01-01         nan         nan         nan          nan         nan   \n",
       "1977-01-01         nan         nan         nan          nan         nan   \n",
       "1978-01-01         nan         nan         nan          nan         nan   \n",
       "1979-01-01         nan         nan         nan          nan         nan   \n",
       "1980-01-01         nan         nan         nan          nan         nan   \n",
       "1981-01-01         nan         nan         nan          nan         nan   \n",
       "1982-01-01         nan         nan         nan          nan         nan   \n",
       "1983-01-01         nan         nan         nan          nan         nan   \n",
       "1984-01-01         nan         nan         nan          nan         nan   \n",
       "1985-01-01         nan         nan         nan          nan         nan   \n",
       "1986-01-01         nan         nan         nan          nan         nan   \n",
       "1987-01-01         nan         nan         nan          nan         nan   \n",
       "1988-01-01         nan         nan         nan          nan         nan   \n",
       "1989-01-01         nan         nan         nan          nan         nan   \n",
       "1990-01-01         nan         nan         nan          nan         nan   \n",
       "1991-01-01         nan         nan         nan          nan         nan   \n",
       "1992-01-01         nan         nan         nan          nan         nan   \n",
       "1993-01-01         nan         nan         nan          nan         nan   \n",
       "1994-01-01         nan         nan         nan          nan         nan   \n",
       "1995-01-01         nan         nan         nan          nan         nan   \n",
       "1996-01-01         nan         nan         nan   0.70833333         nan   \n",
       "1997-01-01         nan         nan         nan   4.32812500         nan   \n",
       "1998-01-01         nan         nan         nan  29.61250000         nan   \n",
       "1999-01-01 52.37855258         nan         nan 108.17500000         nan   \n",
       "2000-01-01 37.09385272         nan         nan  15.03000000         nan   \n",
       "2001-01-01 19.31590395         nan         nan   8.87000000         nan   \n",
       "2002-01-01 12.16813872         nan         nan   8.17500000         nan   \n",
       "2003-01-01 19.81048865         nan         nan  22.51500000         nan   \n",
       "2004-01-01 16.32807033         nan         nan  37.68000000         nan   \n",
       "2005-01-01 22.55441748         nan         nan  39.18000000         nan   \n",
       "2006-01-01 23.61133822         nan         nan  25.54000000         nan   \n",
       "2007-01-01 24.89183834         nan         nan  23.26000000         nan   \n",
       "2008-01-01 10.58953275         nan         nan  12.20000000         nan   \n",
       "2009-01-01 21.05033797         nan         nan  16.78000000         nan   \n",
       "2010-01-01 28.06937567         nan         nan  16.63000000         nan   \n",
       "2011-01-01 23.66553928         nan         nan  16.13000000         nan   \n",
       "2012-01-01 28.01179940         nan         nan  19.90000000         nan   \n",
       "2013-01-01 39.53485221         nan         nan  40.44000000         nan   \n",
       "2014-01-01 39.43238724         nan         nan  50.51000000 30.92000000   \n",
       "2015-01-01 40.79862571         nan 31.27000000  33.26000000 19.06000000   \n",
       "2016-01-01 44.93909238 28.08000000 26.76000000  38.67000000  7.24000000   \n",
       "2017-01-01 66.65391782 53.87000000 81.62000000  69.85000000  9.00000000   \n",
       "2018-01-01 61.80000000 46.88000000 81.63000000  73.35000000  9.81000000   \n",
       "\n",
       "ticker            AADR        AAIT         AAL         AAMC        AAME  \\\n",
       "date                                                                      \n",
       "1962-01-01         nan         nan         nan          nan         nan   \n",
       "1963-01-01         nan         nan         nan          nan         nan   \n",
       "1964-01-01         nan         nan         nan          nan         nan   \n",
       "1965-01-01         nan         nan         nan          nan         nan   \n",
       "1966-01-01         nan         nan         nan          nan         nan   \n",
       "1967-01-01         nan         nan         nan          nan         nan   \n",
       "1968-01-01         nan         nan         nan          nan         nan   \n",
       "1969-01-01         nan         nan         nan          nan         nan   \n",
       "1970-01-01         nan         nan         nan          nan         nan   \n",
       "1971-01-01         nan         nan         nan          nan         nan   \n",
       "1972-01-01         nan         nan         nan          nan         nan   \n",
       "1973-01-01         nan         nan         nan          nan         nan   \n",
       "1974-01-01         nan         nan         nan          nan         nan   \n",
       "1975-01-01         nan         nan         nan          nan         nan   \n",
       "1976-01-01         nan         nan         nan          nan         nan   \n",
       "1977-01-01         nan         nan         nan          nan         nan   \n",
       "1978-01-01         nan         nan         nan          nan         nan   \n",
       "1979-01-01         nan         nan         nan          nan         nan   \n",
       "1980-01-01         nan         nan         nan          nan         nan   \n",
       "1981-01-01         nan         nan         nan          nan         nan   \n",
       "1982-01-01         nan         nan         nan          nan         nan   \n",
       "1983-01-01         nan         nan         nan          nan         nan   \n",
       "1984-01-01         nan         nan         nan          nan  6.40835399   \n",
       "1985-01-01         nan         nan         nan          nan 10.61223421   \n",
       "1986-01-01         nan         nan         nan          nan  9.04493392   \n",
       "1987-01-01         nan         nan         nan          nan  3.80659044   \n",
       "1988-01-01         nan         nan         nan          nan  3.14982608   \n",
       "1989-01-01         nan         nan         nan          nan  2.32975302   \n",
       "1990-01-01         nan         nan         nan          nan  1.39785181   \n",
       "1991-01-01         nan         nan         nan          nan  0.69892591   \n",
       "1992-01-01         nan         nan         nan          nan  1.51433946   \n",
       "1993-01-01         nan         nan         nan          nan  1.63082711   \n",
       "1994-01-01         nan         nan         nan          nan  2.09677772   \n",
       "1995-01-01         nan         nan         nan          nan  2.15548749   \n",
       "1996-01-01         nan         nan         nan          nan  2.85161770   \n",
       "1997-01-01         nan         nan         nan          nan  4.71821581   \n",
       "1998-01-01         nan         nan         nan          nan  4.54301839   \n",
       "1999-01-01         nan         nan         nan          nan  2.15548749   \n",
       "2000-01-01         nan         nan         nan          nan  1.86380242   \n",
       "2001-01-01         nan         nan         nan          nan  2.05391026   \n",
       "2002-01-01         nan         nan         nan          nan  1.51899897   \n",
       "2003-01-01         nan         nan         nan          nan  2.79570362   \n",
       "2004-01-01         nan         nan         nan          nan  2.88889374   \n",
       "2005-01-01         nan         nan 35.76072005          nan  2.51613326   \n",
       "2006-01-01         nan         nan 51.85015549          nan  2.75842757   \n",
       "2007-01-01         nan         nan 14.16371007          nan  1.30466169   \n",
       "2008-01-01         nan         nan  7.44292854          nan  0.69147070   \n",
       "2009-01-01         nan         nan  4.66025539          nan  1.19283355   \n",
       "2010-01-01 28.82785959         nan  9.63825546          nan  1.89175945   \n",
       "2011-01-01 27.36247977         nan  4.88171380          nan  1.85276561   \n",
       "2012-01-01 30.02536396 27.10695167 12.99864622  82.00000000  2.97706591   \n",
       "2013-01-01 36.74348283 31.27057728 24.31228275 930.00000000  3.96461868   \n",
       "2014-01-01 36.88899069 32.90504993 51.89970750 310.12000000  3.94778326   \n",
       "2015-01-01 38.06921608 30.53000000 41.33893271  17.16000000  4.91270038   \n",
       "2016-01-01 39.81959334         nan 46.08991196  53.50000000  4.05320152   \n",
       "2017-01-01 58.83570736         nan 51.80358470  81.60000000  3.37888199   \n",
       "2018-01-01 52.88000000         nan 37.99000000  67.90000000  2.57500000   \n",
       "\n",
       "ticker         ...            ZUMZ         ZUO        ZVZZC     ZVZZCNX  \\\n",
       "date           ...                                                        \n",
       "1962-01-01     ...             nan         nan          nan         nan   \n",
       "1963-01-01     ...             nan         nan          nan         nan   \n",
       "1964-01-01     ...             nan         nan          nan         nan   \n",
       "1965-01-01     ...             nan         nan          nan         nan   \n",
       "1966-01-01     ...             nan         nan          nan         nan   \n",
       "1967-01-01     ...             nan         nan          nan         nan   \n",
       "1968-01-01     ...             nan         nan          nan         nan   \n",
       "1969-01-01     ...             nan         nan          nan         nan   \n",
       "1970-01-01     ...             nan         nan          nan         nan   \n",
       "1971-01-01     ...             nan         nan          nan         nan   \n",
       "1972-01-01     ...             nan         nan          nan         nan   \n",
       "1973-01-01     ...             nan         nan          nan         nan   \n",
       "1974-01-01     ...             nan         nan          nan         nan   \n",
       "1975-01-01     ...             nan         nan          nan         nan   \n",
       "1976-01-01     ...             nan         nan          nan         nan   \n",
       "1977-01-01     ...             nan         nan          nan         nan   \n",
       "1978-01-01     ...             nan         nan          nan         nan   \n",
       "1979-01-01     ...             nan         nan          nan         nan   \n",
       "1980-01-01     ...             nan         nan          nan         nan   \n",
       "1981-01-01     ...             nan         nan          nan         nan   \n",
       "1982-01-01     ...             nan         nan          nan         nan   \n",
       "1983-01-01     ...             nan         nan          nan         nan   \n",
       "1984-01-01     ...             nan         nan          nan         nan   \n",
       "1985-01-01     ...             nan         nan          nan         nan   \n",
       "1986-01-01     ...             nan         nan          nan         nan   \n",
       "1987-01-01     ...             nan         nan          nan         nan   \n",
       "1988-01-01     ...             nan         nan          nan         nan   \n",
       "1989-01-01     ...             nan         nan          nan         nan   \n",
       "1990-01-01     ...             nan         nan          nan         nan   \n",
       "1991-01-01     ...             nan         nan          nan         nan   \n",
       "1992-01-01     ...             nan         nan          nan         nan   \n",
       "1993-01-01     ...             nan         nan          nan         nan   \n",
       "1994-01-01     ...             nan         nan          nan         nan   \n",
       "1995-01-01     ...             nan         nan          nan         nan   \n",
       "1996-01-01     ...             nan         nan          nan         nan   \n",
       "1997-01-01     ...             nan         nan          nan         nan   \n",
       "1998-01-01     ...             nan         nan          nan         nan   \n",
       "1999-01-01     ...             nan         nan          nan         nan   \n",
       "2000-01-01     ...             nan         nan          nan         nan   \n",
       "2001-01-01     ...             nan         nan          nan         nan   \n",
       "2002-01-01     ...             nan         nan          nan         nan   \n",
       "2003-01-01     ...             nan         nan          nan         nan   \n",
       "2004-01-01     ...             nan         nan          nan         nan   \n",
       "2005-01-01     ...     21.61000000         nan          nan         nan   \n",
       "2006-01-01     ...     29.54000000         nan          nan         nan   \n",
       "2007-01-01     ...     24.36000000         nan          nan         nan   \n",
       "2008-01-01     ...      7.45000000         nan          nan         nan   \n",
       "2009-01-01     ...     12.72000000         nan          nan         nan   \n",
       "2010-01-01     ...     26.87000000         nan          nan         nan   \n",
       "2011-01-01     ...     27.76000000         nan          nan         nan   \n",
       "2012-01-01     ...     19.41000000         nan          nan         nan   \n",
       "2013-01-01     ...     26.00000000         nan          nan         nan   \n",
       "2014-01-01     ...     38.63000000         nan          nan         nan   \n",
       "2015-01-01     ...     15.12000000         nan          nan         nan   \n",
       "2016-01-01     ...     21.85000000         nan 100.01000000 14.26000000   \n",
       "2017-01-01     ...     20.82500000         nan 100.10000000         nan   \n",
       "2018-01-01     ...     24.30000000 26.74000000          nan         nan   \n",
       "\n",
       "ticker             ZX      ZXYZ_A        ZYME        ZYNE          ZZK  \\\n",
       "date                                                                     \n",
       "1962-01-01        nan         nan         nan         nan          nan   \n",
       "1963-01-01        nan         nan         nan         nan          nan   \n",
       "1964-01-01        nan         nan         nan         nan          nan   \n",
       "1965-01-01        nan         nan         nan         nan          nan   \n",
       "1966-01-01        nan         nan         nan         nan          nan   \n",
       "1967-01-01        nan         nan         nan         nan          nan   \n",
       "1968-01-01        nan         nan         nan         nan          nan   \n",
       "1969-01-01        nan         nan         nan         nan          nan   \n",
       "1970-01-01        nan         nan         nan         nan          nan   \n",
       "1971-01-01        nan         nan         nan         nan          nan   \n",
       "1972-01-01        nan         nan         nan         nan          nan   \n",
       "1973-01-01        nan         nan         nan         nan          nan   \n",
       "1974-01-01        nan         nan         nan         nan          nan   \n",
       "1975-01-01        nan         nan         nan         nan          nan   \n",
       "1976-01-01        nan         nan         nan         nan          nan   \n",
       "1977-01-01        nan         nan         nan         nan          nan   \n",
       "1978-01-01        nan         nan         nan         nan          nan   \n",
       "1979-01-01        nan         nan         nan         nan          nan   \n",
       "1980-01-01        nan         nan         nan         nan          nan   \n",
       "1981-01-01        nan         nan         nan         nan          nan   \n",
       "1982-01-01        nan         nan         nan         nan          nan   \n",
       "1983-01-01        nan         nan         nan         nan          nan   \n",
       "1984-01-01        nan         nan         nan         nan          nan   \n",
       "1985-01-01        nan         nan         nan         nan          nan   \n",
       "1986-01-01        nan         nan         nan         nan          nan   \n",
       "1987-01-01        nan         nan         nan         nan          nan   \n",
       "1988-01-01        nan         nan         nan         nan          nan   \n",
       "1989-01-01        nan         nan         nan         nan          nan   \n",
       "1990-01-01        nan         nan         nan         nan          nan   \n",
       "1991-01-01        nan         nan         nan         nan          nan   \n",
       "1992-01-01        nan         nan         nan         nan          nan   \n",
       "1993-01-01        nan         nan         nan         nan          nan   \n",
       "1994-01-01        nan         nan         nan         nan          nan   \n",
       "1995-01-01        nan         nan         nan         nan          nan   \n",
       "1996-01-01        nan         nan         nan         nan          nan   \n",
       "1997-01-01        nan         nan         nan         nan          nan   \n",
       "1998-01-01        nan         nan         nan         nan          nan   \n",
       "1999-01-01        nan         nan         nan         nan          nan   \n",
       "2000-01-01        nan         nan         nan         nan          nan   \n",
       "2001-01-01        nan         nan         nan         nan          nan   \n",
       "2002-01-01        nan         nan         nan         nan          nan   \n",
       "2003-01-01        nan         nan         nan         nan          nan   \n",
       "2004-01-01        nan         nan         nan         nan          nan   \n",
       "2005-01-01        nan         nan         nan         nan          nan   \n",
       "2006-01-01        nan         nan         nan         nan          nan   \n",
       "2007-01-01        nan         nan         nan         nan          nan   \n",
       "2008-01-01        nan         nan         nan         nan          nan   \n",
       "2009-01-01        nan         nan         nan         nan          nan   \n",
       "2010-01-01        nan         nan         nan         nan          nan   \n",
       "2011-01-01 2.99225131         nan         nan         nan          nan   \n",
       "2012-01-01 2.99000000         nan         nan         nan          nan   \n",
       "2013-01-01 2.60000000         nan         nan         nan          nan   \n",
       "2014-01-01 1.43000000         nan         nan         nan 100.00000000   \n",
       "2015-01-01 0.79000000 14.10000000         nan 10.07000000 100.00000000   \n",
       "2016-01-01 1.19890000  8.00000000         nan 15.59000000 100.00000000   \n",
       "2017-01-01 1.21650000         nan  7.59310000 12.52000000  25.00000000   \n",
       "2018-01-01 1.38000000         nan 15.34000000  9.17000000          nan   \n",
       "\n",
       "ticker             ZZZ  \n",
       "date                    \n",
       "1962-01-01         nan  \n",
       "1963-01-01         nan  \n",
       "1964-01-01         nan  \n",
       "1965-01-01         nan  \n",
       "1966-01-01         nan  \n",
       "1967-01-01         nan  \n",
       "1968-01-01         nan  \n",
       "1969-01-01         nan  \n",
       "1970-01-01         nan  \n",
       "1971-01-01         nan  \n",
       "1972-01-01         nan  \n",
       "1973-01-01         nan  \n",
       "1974-01-01         nan  \n",
       "1975-01-01         nan  \n",
       "1976-01-01         nan  \n",
       "1977-01-01         nan  \n",
       "1978-01-01         nan  \n",
       "1979-01-01         nan  \n",
       "1980-01-01         nan  \n",
       "1981-01-01         nan  \n",
       "1982-01-01         nan  \n",
       "1983-01-01         nan  \n",
       "1984-01-01         nan  \n",
       "1985-01-01         nan  \n",
       "1986-01-01         nan  \n",
       "1987-01-01         nan  \n",
       "1988-01-01         nan  \n",
       "1989-01-01         nan  \n",
       "1990-01-01         nan  \n",
       "1991-01-01         nan  \n",
       "1992-01-01         nan  \n",
       "1993-01-01         nan  \n",
       "1994-01-01         nan  \n",
       "1995-01-01         nan  \n",
       "1996-01-01         nan  \n",
       "1997-01-01         nan  \n",
       "1998-01-01         nan  \n",
       "1999-01-01         nan  \n",
       "2000-01-01         nan  \n",
       "2001-01-01         nan  \n",
       "2002-01-01         nan  \n",
       "2003-01-01         nan  \n",
       "2004-01-01         nan  \n",
       "2005-01-01         nan  \n",
       "2006-01-01         nan  \n",
       "2007-01-01         nan  \n",
       "2008-01-01         nan  \n",
       "2009-01-01         nan  \n",
       "2010-01-01         nan  \n",
       "2011-01-01         nan  \n",
       "2012-01-01         nan  \n",
       "2013-01-01         nan  \n",
       "2014-01-01  0.50000000  \n",
       "2015-01-01  1.00000000  \n",
       "2016-01-01 25.00000000  \n",
       "2017-01-01  0.01000000  \n",
       "2018-01-01         nan  \n",
       "\n",
       "[57 rows x 11941 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing = pd.read_csv('../../data/project_5_yr/yr-quotemedia.csv', parse_dates=['date'])\n",
    "pricing = pricing.pivot(index='date', columns='ticker', values='adj_close')\n",
    "\n",
    "\n",
    "pricing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dict to DataFrame\n",
    "The alphalens library uses dataframes, so we we'll need to turn our dictionary into a dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98065125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.95001565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.93176312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.89701433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.85838968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date ticker sentiment      value\n",
       "0 2016-01-01   AMZN  negative 0.98065125\n",
       "1 2015-01-01   AMZN  negative 0.95001565\n",
       "2 2014-01-01   AMZN  negative 0.93176312\n",
       "3 2013-01-01   AMZN  negative 0.89701433\n",
       "4 2012-01-01   AMZN  negative 0.85838968"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarities_df_dict = {'date': [], 'ticker': [], 'sentiment': [], 'value': []}\n",
    "\n",
    "\n",
    "for ticker, ten_k_sentiments in cosine_similarities.items():\n",
    "    for sentiment_name, sentiment_values in ten_k_sentiments.items():\n",
    "        for sentiment_values, sentiment_value in enumerate(sentiment_values):\n",
    "            cosine_similarities_df_dict['ticker'].append(ticker)\n",
    "            cosine_similarities_df_dict['sentiment'].append(sentiment_name)\n",
    "            cosine_similarities_df_dict['value'].append(sentiment_value)\n",
    "            cosine_similarities_df_dict['date'].append(file_dates[ticker][1:][sentiment_values])\n",
    "\n",
    "cosine_similarities_df = pd.DataFrame(cosine_similarities_df_dict)\n",
    "cosine_similarities_df['date'] = pd.DatetimeIndex(cosine_similarities_df['date']).year\n",
    "cosine_similarities_df['date'] = pd.to_datetime(cosine_similarities_df['date'], format='%Y')\n",
    "\n",
    "\n",
    "cosine_similarities_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format\n",
    "In order to use a lot of the alphalens functions, we need to aligned the indices and convert the time to unix timestamp. In this next cell, we'll do just that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>factor</th>\n",
       "      <th>factor_quantile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>asset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1994-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.53264104</td>\n",
       "      <td>0.44665176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.22211880</td>\n",
       "      <td>0.64730294</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>0.17159556</td>\n",
       "      <td>0.46696523</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1995-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.32152919</td>\n",
       "      <td>0.43571094</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.28478156</td>\n",
       "      <td>0.62523346</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1D     factor  factor_quantile\n",
       "date       asset                                       \n",
       "1994-01-01 BMY   0.53264104 0.44665176                1\n",
       "           CVX   0.22211880 0.64730294                5\n",
       "           FRT   0.17159556 0.46696523                3\n",
       "1995-01-01 BMY   0.32152919 0.43571094                3\n",
       "           CVX   0.28478156 0.62523346                5"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import alphalens as al\n",
    "\n",
    "\n",
    "factor_data = {}\n",
    "skipped_sentiments = []\n",
    "\n",
    "for sentiment in sentiments:\n",
    "    cs_df = cosine_similarities_df[(cosine_similarities_df['sentiment'] == sentiment)]\n",
    "    cs_df = cs_df.pivot(index='date', columns='ticker', values='value')\n",
    "\n",
    "    try:\n",
    "        data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing, quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except:\n",
    "        skipped_sentiments.append(sentiment)\n",
    "\n",
    "if skipped_sentiments:\n",
    "    print('\\nSkipped the following sentiments:\\n{}'.format('\\n'.join(skipped_sentiments)))\n",
    "factor_data[sentiments[0]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format with Unix Time\n",
    "Alphalen's `factor_rank_autocorrelation` and `mean_return_by_quantile` functions require unix timestamps to work, so we'll also create factor dataframes with unix time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "unixt_factor_data = {\n",
    "    factor: data.set_index(pd.MultiIndex.from_tuples(\n",
    "        [(x.timestamp(), y) for x, y in data.index.values],\n",
    "        names=['date', 'asset']))\n",
    "    for factor, data in factor_data.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor Returns\n",
    "Let's view the factor returns over time. We should be seeing it generally move up and to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb289fb2e8>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb28798f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in factor_data.items():\n",
    "    ls_factor_returns[factor_name] = al.performance.factor_returns(data).iloc[:, 0]\n",
    "\n",
    "(1 + ls_factor_returns).cumprod().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basis Points Per Day per Quantile\n",
    "It is not enough to look just at the factor weighted return. A good alpha is also monotonic in quantiles. Let's looks the basis points for the factor returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb28a83b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb2874b048>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb2881f2b0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb289b9eb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb28980be0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb28980cf8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb289f6f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb28977ac8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb289edda0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb288e93c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb28970860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb28933588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbb2888d4a8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb288cb908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbb288b05f8>]], dtype=object)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb28754320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qr_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in unixt_factor_data.items():\n",
    "    qr_factor_returns[factor_name] = al.performance.mean_return_by_quantile(data)[0].iloc[:, 0]\n",
    "\n",
    "(10000*qr_factor_returns).plot.bar(\n",
    "    subplots=True,\n",
    "    sharey=True,\n",
    "    layout=(5,3),\n",
    "    figsize=(14, 14),\n",
    "    legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turnover Analysis\n",
    "Without doing a full and formal backtest, we can analyze how stable the alphas are over time. Stability in this sense means that from period to period, the alpha ranks do not change much. Since trading is costly, we always prefer, all other things being equal, that the ranks do not change significantly per period. We can measure this with the **Factor Rank Autocorrelation (FRA)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb28d2de48>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OsWXxSbJOuzF8RiBKk+5/kFwJB3IO8MK+F5i9qgS7Ghk+3/5MKkd45+hnfG/+JwujF9LXqW+3lpmVWMr27xNpbNIw8vZAclPKOXeimCilCalFCcQVxBFXEEd8YTxVjVUAuFu6M8JjBAOdBxLQ5MKOrQsxtVVTU1ZKqasdfY+U8rTn0/hZ+3F/7/uZ4DsBhaz146q6rIG0hGJc/a3JO1vB4U1pRE31B6BJ18QXx75gyYkl+Fr7smjsIoLtgi/kTciuoFGjA+BIqo4XBr/A3H1z+SHxB+4Jv6db2wjgyIY1JO3fzXCnLEobTDiw6mccvLwJjBzaYb6q7dvR1dRgPeXmbrepPYwrn7uAXC6nylSgqK6+1qb0OJcrDPn5xyiUCyLsQnvQqrYRMhl2cjllwpT5QwX51Q2c8lYSOMiZg2vPcuS3tL90/+Lsala9dYTEA3n0v96bqU/3R+1ghtrBjKlP96f/9d4k7s9l1duxlOR0/Nmora7mu+9XcvBU+oWHUXfSpG3ig9gPeGj7Q4xIVTL8pBbHRx7BvHcE9/W+j6UTliKE4O7Nd/N1wtdoddq/XKZWq+Pg2lQ2fHIMUwsl058fiE1fOVuVu2ms13LvZ09xx+938EHcByQUpOKtiuJ6xye5x+sbbnX6Al/pPopzwtj++ffolGaIGx4EoHHAQEJSG/gg/CVkQsbcfXOZvG4ya1PW0qRtamFD4r4cJElizN2hhAx15ejWDPLOVpBdlc3dm+/mmxPfcEvgLSy/aXkLUQA4nKZ3TnjambEmPpuJfhMZ4zWGT49+SkpZyl9un+acjYth7/KlBA8ZiolrLkEeabi42vLHZx9QlNHx57Ri3TqUHh6YDxzYrTZ1xD9yP4ZrQbWZAlVt3bU2o8eR29igrei6KykhZRMAfbxH95RJHWLv4ERqdTlO+UeY1n8sS/ans/nfI5DJBYc3pqHTSkRO8r2s2SaSJHFydw77V6eiMlcw+Ym+eIbYtUgjl8uImuqPR7At275PZNWCWIZPDyRshFursvLPprDy3bdoKiti9+ZlLLIJQwofxYBePkT62tHPywZT5ZX3OLKqsnhuz3OcKD7BbOeJTFm0C5OwMBwefuhCmgjHCFZNWsUbB9/g06OfEpMXw9sj3sbJ/MpcYZXFdWxdcoqCtEpCR7gReKOaRWc+ZtWZNWilJqabvkhw9nXENniirfOlSmvFxcnMxYYDBpcdJrI8h01ON5B2qIJ7LZwoVMpw1Wrpf6yGNfeu4c/MP/kq4StePvAyXx7/kvvC72Nq4FTkKEjcl4tXqD3WjmYMnx5IdlIZGxbH8mPYa+jkGt4d+S43+LS99jYmrZRgZytu6e/O238kkVZcw8tRLzN1/VTm7pvLshuXoZQrr6h9mlOclcFvn7yHs68/TSMcmHPSGb+mJn7wESw/YM66d9/gjrc+xFxt3SpvU14eNQcP4fCvfyFkV+893thj6CK1ZkrMa+uvtRk9jtza+rJ6DAn5sZhIEsH+N/agVe3j7B8CQMGpWJ6bEIyJQsb8308z5q4QQoe5Evt7Ogd/Patff9IF6mua+GPRCfasSMY92JaZL0W2EoXmeIbaMeulSNwDbdi97Aybvz5JfY3+rVbS6YjdtJbl856hoaGRna7X4xA+kN7lJwjf9xnxvyzhgS+3E/HqVqYvOsC7W5LYnVxEdYOm3fIu5Y+0P5ixcQbpFem8H/0et2+sQqqpxW3hAoSy5UPNysSKhdELeX3o65woPsG0DdPYnbW7y2WdJyW2gF/ePExZfi0DZ7uw23cFkzbdxOqU1Wir+hPBAiZMjMKpwY5t0x8iYd40Trw6vtWx404fBlceJWj4aDa8+xAPRvuRrHQjLyMNZZ8IKtatQyAY4z2GXyb+wudjPsfR3JE3Y95kwpoJ/PDbr9RUNBI+Ur/0SaNoJGtgDJpywaicGayavKpdUdBodcSllxLpa8fUfu7IBPwan4OdqR2vRL1CUmkSixIWXXbbXEpdVSXr3n0DpUqF4pZ+vHLqM1w1Ws6amHCsLIabn36RmvIyNn7wNlpNU6v8Fes3gCRdVTcSGIWhy9SZq7Coa/2P+1/jcl1JCTVZhGKCUnV5K6W7C4fQMORaHXnncnCyMuXfYwLZkVTIruQiRt3Ri/CR7hzdlsm+VSmdikNuShm/vHmYjJMlDLs1gImPRmCuNunUBnO1CRMf68PQWwJIP17ML/MPk3Ysg7ULX2P3j0vw6z+Q3aF3Ydc7kvtfeon7P/qKvqPH0KfmNPfmLON+cQRZdSmLdp9jzreH6fPaVm7+bB9v/X6a7YkFVNS2/tzVNtXy8v6XeXbPswTYBLB68moij9ZSvWMHjv/5D6qAticCCCGYGjiVFRNX4GzuzGM7H2Ph4YU0ahs7rWdTg5adP55m6zenMHdSkHX9bh46dzsbz25kWuA0pjp8SlX2VF4cP4I+w9xRquScO5SPlamy1WEq07H7m0+xtLVn/H0PY2WqZESgAxmmnui0GmqjBtOQkkL9qcQLdkd7RPPThJ/4etzXeKo9Sd5fRI2qnJ1s4FjhMWb9Notfqr5D17sY1/RQtJntz2pKzKukplFLpK8dTmpTooMc+TU+G51O4jqv65jsP5klJ5ZwouhEp+3SHlqNhk0fLaC6pBjz6ZHMT3yPoTbB/JqTh4tQ8b1Kh4ta4vqHniD79En+/P7rFvklSaJi7VrMBw7ExLPnZkq1hdGV1EUaLMyxzL68Qdl/InIbG3SVlUgaDULR8cejqaGGRKmBmZb+V8m61pj6+6Oub6CwUkBlHnOG+rD8SCavb0pk2H+iiZ4VhFwu4/jOLLQaiZGzghCylq4enVZH7O/pxP6ejtrBjGnPDsDJW91OiW0jZIJ+471wC7Rh06cb+XXBJwjRwOh7HsZ3xDj+8+Z2nuqv73nYuLgy/qEnGDJtFofXr+Hkzi0M1sUyO2okJgPGcaJaxbHkEvbvzCStMZNz2jKcVUnUWJ8hXoRyWGlLmeNaUBQhqxzLyczrmbNnLwt+W0CGkz+zs9yQ3tgGgJ2FCV/fOQA/x5bTOP2s/fj5pp/5IPYDfjr9E3EFcbwT/Q4+1j5t1q84u5qt35ykrKCW8tCzLLb6HGWxkttDbufusLtRSDaMWLiD+V6xhK54BjT1BJnMJunQUIblzsRUXtPifvuynCkrtOfWwAxUn+lDYAyX4E7LIVTKFRSZm+BuYkLFunWYhYddbGchiHKLIkTeh5/XHSIvNIEfjy4BwNHMkcXjFzPAfiAr3zrCzh+SmDUvElOL1u4g/fiCRN+3Z3GuRMdDvUfyabEVh456MXRAIM9HPs/h/MPM3TeXVZNWYapoKTLnjhZxaP1ZgiKd6T3aE5VZ6+/K7h+XkHkyAaubI3kvdzGjPEfxvu1gTI5uZbbPBN5LW8epxFWEjZpHUVYGR9avxsHLl77j9b3vumPHaMzIwP7BB9v72PUYRmHoIhoLSyzrJLRaLfJujK30d+PCIrfKyhZzztsi+dxmGmSCCJerNyh2KSY+PqjrGsmxsETKPIRJ+FRenhjK3d8d4dv9aTw80p9h0wOQKwXxWzLRaXSMmt0LmUEcqkrr2fbtKfJSKwge4kL0rCBMTK/sa6HVaEg+tJby3NWozB2QZLeQedqNQrsiJAkifVu2p9rBibH3PcKQqTPYv2oliXu2odu/C1OrEAaLgcjkDijkGixpILs2molmO8FuD3/a2WIvCe6pDcDcypM0JyWDflyNUkDy3U9yo93F0A7rjubw6sZElt4zqNXYh0qu4oXBLxDlFsW8/fOYsWkGcwfP5Wb/my+kPT/esm91Mo2Kev4IWUKZfTZzes3hrtC7sDezB+CDjYd5R/qQmwpjwGsoOIcSXinj1G4Tkiweoq//xQHWzPxa4uOy6NfLBu/IiwPCoiyDB+t+4x3LyWQmnSJ4zHVUbtqE87PPIExa9txO7slBJhO8MOcRpjeOZm/2Xm4PuR07U30bj70nlNUL49j7SzLj7g3jUmLSSnm17leqjxVhYq/EdufvzKuvhzuWcjbAH/OBA1kQcBNPln/Dx/Ef81zkcxfyphwpYNt3iZhZKYnZkMbRbVlEjPagz3WemFrqRShhx2aObt6IanAAnzatYpz3OBaOWIjysL5XMK3vwyw6t47vsrbwHvMYPutOSrIy+PP7r7Bz88ArPIKKtesQZmZYXX99Fz+B3YdRGLqIpLZGoYPCwjxcXf934/JcEIby8k6F4XjGTgD6BEzscbvaQ25lhY1MSQZyyk7txS58KqOCnRgb4synO1KY2s8dZ7UpQ6b4I1PIiP0tHZ1W4rq7epGeUMLOH0+j00qMvSeU4MEuV2xHRWEBv33yDnkpZwgfPZ7Rcx7g7NEy9qxIpjGtgmCVgj6e+raVJImKwjpyU8rJTSknJ6WM6tJglBaeoD1KU+1RdNpEPL29GCE24DDsTn464Mdq7X9Yav8qwy3cebPBFLuCWCjcSWmyBQVnrXG5JZjn++WDtx/Y+oAQ+DhY8MamRLafLmRcaNuxgEZ5jmL1pNU8v/d55u2fx8Hcg8wbMg9Fk4p138RQcrqRTJtEDvdaz7Q+NzM7ZDY2pjYX8pee2cvM2LtxkZfB2Ndg6BMgk+EAuGTFcbJ4AH0eexQhEzTU1rL5mUexdXVnxNyPQdXsTVzTSMGHwxmsOklsjjuKux5G+8dmqnbvRt1sYZemUUvSwTz8+jliYa2iD31aBcFz8lYz8EYfjmxKw7ePY4v1JjqdRMG5k4w8uocmUxm+08wRjx3koy82ULT/EA+oK6jcuAnzmhq+AvJtvidhyFm8Rt5IjiqI3RvzcQ2w4aZHI6goqiPO0Ns8viOL8JHuOHlVs2PJIuR+jnxtu4MJPhN4a8Rb+um2tSUg5FhauTFd5cHShiyyKjLxtPbixsefYdlL/2XjRwu47eW3qfzjD9TjxyG3vPpuWqMwdBG5jX5xdk52+v+4MOhnRnRlkVtCSSJOWgln52sbmdLRyRk0VRQkn+C8lM2bGMK4D/aw8I8kPpjZFyEEgyf5IZcLYjakUZRVRWluDY5eVoy/PwwbpytfOHTm4D62ff0pkiRx07+fpdfQaABChprh4mfNVwsOM7lKyf5lZ9A06shNKae2Uu/TN7NS4hZoQ79xtrgF2mDvdhP1NVXEr1jE0Z07Wabri4N5A3Hm39Mr51aeMHuFe2+dql/0pdPScGQrhb8+i0WAOTbqY7D+T71RanfwHsoc7+Gsc3ThjU2JjAh0aHf2k7OFM9+M/4bFJxbz5fEvyUwpJvLkFOT1KhL8tzNofAAvhfyKlYnVxUw6Hez/EJsd86nBjoJb1+MWPqLFfcNHurP9u0Syz5ThGWLHn0u/prqkhFmvv4NSdckYgMKEgus+InDVDGJxp0glR+XoQMXadS2EISW2kIZaDeHRbcbbvMCACd6kJxSze9kZXAOssbDWL+5LyS/ntbyPqS9UohrrSVFFJs4mJoy8ZQy35JvRd1oE0/u5Up90horDBzi5aRFWu/cTd0xDUvDt2NVm0D8vlbotWThMmsQND/WmJLeauD8yiN98ksbKn8HUnF89UpkUOJnXh75+ccFcbQmY24MQ3BFwCz+e+pif4j/mhdHvozI3Z8qz81g29ynWzZ/HoJpqrKdMuVCfkuoGfj+Rx9T+HliqevbRbRSGLqKy079xFOVldZLyn03zHkNnJDQUE6G0uarT6NrCwTcAWXI8hXklhDTVgdIMb3sLHoj25fM/z3LHEC8GeOslY+CNvsgUMg6tPUvfsZ4MmeKPXHFl9jc11LNr6Tck7NiMS0AQNz3xLDbOLXsdJrYqvjOr4xE3e5IO5mNpq8Kjl14E3AJtsHE2b+XiMdNVMax8MQMGWnLM/Ql2/bYG30Zo4As0G53ZWV2CT0QEbgFBFHzwPcLMAtclGxCODlCUBBn79UfaHhQnVvGrypaPqsfxw582PDi+X7v1kcvkPNznYfpZDOLAR/k0Kuqwv7WST6JfxVx5iXBW5cOvD0Labv7QRRETNo/Xw4e1uqd/f0f2rVRyck8OjbUpnNq1ncFTZ+AW1KtVWoDgPoP5eO0k1IpU0vdtInLyZEqX/oCmpASFvd5tdXJPDrYu5rgF2bR5jwv1kcsYe08oK+cfYdfPZ7htf9ftAAAgAElEQVTxkd4IIajd+T62Jyupt3Nge60lVUUBeL/5EkOmzcLPwYLV8dnMGOSJWXgYZuFhBE2KZMG3ixieNh0X23oGmiXScOgwuZvWITU1YXPrrdi7WTL6Dn9yTn1BQ7UGpXI6M0/ZEW7rQU1II2p7Q/iU88IAOAffxI0xb7E2ayeP1JdjY2qDrYsbE598njVvvkRCkBdhgwZdqM/K2GwWbk5isJ89Qc5WbVW52zAKQxdRO+i74RWFedfYkp6lq8JQWppKlhym2wZ3mO5qYOrnh9Xxg+TXmENOPPjoH1D/GhXAmrgcXt2QyLpHhyE3jCv0H+9NxCgPFH9htXJxZjqbPn6HkuxMBk2exrCZdyJvY7D+aGY5DZJEvyl+DPWxQ6GUdbymorEWVtwOTXWY3r+RILUTD9d+yMMOtxFQ6MyJPw+TsP13jm/dAIBVXQOe14+Cc8l4qJRYOIeCcyhEPqAP35F1GOXe93gmZSWV+zdRpX0Iq5FPgHnbbkJJkij8Q46ZsGD2s8NxdGs9t56U7bD2IWisYa3n8zx7LoKd49oWHIVSrl94tu0MabHLcfT27TAMhEoh54T3ndxY9DKZyWcZ+9+ZlC75lspNm7CbM4eizCoK0ysZPiOwS2tT7FwtGDLFj/2rUzl9II9Q/xL89iwht8QG8fgdVO3ZSqBVMdnpZ/nl1eeZ6OrPr1IoGcUReDsYXDgJdgxPm0667UlCHuyNt+/7SJJExuw7KfzgQ6zGj0dmackfn39ASW4G2wcUEN03g6iC3pzen8fpfXkER7nQ/3pvbGouCgNqN+4WNmyQNPxy5hce6qNfd+Lm5EZIbgmJbvYcWL2c4bPuRKeTWHY4g8G+dj0uCmAUhi5j66J3H9WVFF5jS3qWrgrDiWT9gynCM7rHbeoME18f1HUNFDToB6CFQRgsVApeuLEX/15xjFWxWcyKvBg/6UpFQZIkErZvZtfSxZiYmzNt7uv49OnfbvrDaSXIBAzwtu08bIYkwYbHIO843LYCnHpxNGMHWrnEkCET6OvUFxOLYZzYncGw0fVkLXqHCk83UtJTSfxoAQC2bh54hIThERKOR0g4aq/BcMcq8pMOkbDsJcbHfAhHF8Og+yDqcbBsueHLqb25ZJ4qZcTMoNaioGmEHa/Bwc/AKYy8cZ/z7Pd5zBjoiadd+6640BGuxKz9HElbxfSX3kCu6HjR2JAAJzYkjGCg9ggV+9/HNDyc8nXrsZszh5O7s1GYyOg1pOvjQX2u8yTteDH7Vibj5rOAohNqqmwdqDJVolDIud41GdkDi0g4kU7M+jVMrdjI6tdOcMu9d1Na6MChtefw6WvPny57ef3wr/Rz7YutqS0uL84lbdqtFH/+OakBXqQcPkBsr1JGj7yVZwc9jRCCQTf5Er81k8R9uSQdyCNQPYYBvTIuuDwDvUcxPOcPliUt4+7wu1HJVVRu3IB3UTncPImYtb/g4OVNnn0vskrrePb6tnta3Y1xHUMXcXXXx91pKiu5xpb0LDJLS5DLOxWGhLwY5JJEaOCkq2RZ+6h8fbGubaRRUlB5JqbFtcl93BjkY8s7W85Q0Q3rUPb8/B3bv/kc95Aw7nrn0w5FAeBweilhbtZd8wnv+wBOroGxr0CwfmFWbEEsKrmKMHv9zJrIyX6ozE05vbmMII1g1sdf8ei3K7j9zfeJvuMebF1cST64jz8+e5/Fj97Dt/95kIJzqbj0GkJi9BeMb1hIsdtoOPApfNQb/ngeKnMBqCiqZf+aVDx62dJ75CX++5Kz8O14vSgMuh8e2MGHx/W9n8eu6zh4Yl7yEXRNKZhZj8Deo/P4VVH+9hwz1YdYSU8+i3U/RxpOn6byeCLJRwoIHOSMyrzrK5KFTDBmTghoG9l+eiJNZXKKp80hOWY/vr38Ucm1KLWVDLhpCg9+toSsXhOoLSvh1wWvsnvpGzh5FXH9fWHMj36TisYK3jj0BpIkYRoais2MGZzasJZDq5eT6l7NoEnTeHbQsxd6M5a2pkTPDOLON6PoM8aTc5VhLI+ZQtzmdL1xvtHcXVZKaX0pG89uRJIkytetw7xfP8b/5xnce4Wy5cuPWbPlIA6WKq4Pu/IJEpeDURi6iLu74W2z8n97LYMQQr/6uZOwGAmVaQRJCszacUlcTZQeHqib9LF/Cs5ejIAK+vq8OjmM8tpGPtqe/JfKkXQ6Tv65jYBBUUx74TUsbDrema9Bo+VoZnmraaptkvQ77HgDek+HYf+5cDquII4+jn0uhGYwtVASZpFGqcqTxgdeR2Fri1yhwDUwmEGTpzH1uVf415Jl3LnwE0bPeQCtRsOqN14kNzmJh0f6U2MdxOzyB9E8EgPht8Dhr+HjPug2PsmOb44hkwmuuyuk5VqPhJXwVTSUpsHMn+Cm90mr0LEmPoc7Bnvhat1++PGq0mJ2fPsldu7+aKW+pCd0/mIV4W6N3FyNxsaNDK0v6vq1oFBwYkUMmkZdp4PObaGuTWC4+VcUyEI54T8Rm0Eh1FaU02tQpD5BjT5Eh8LEhJE338wZuztRmI9DadJE5vEfWTb3P3CmkH9F/IttGdv4Pe13AHS3TOa4hwMmmnpCZ03hqQFPtenisrBWMewWf+5yeggftzKObEqnuqwefEYQWd9AiIk9S08tpfZEAo2pZ7GeOgW5Qsnkp+ZiYmmFw6FlzAxTY3KF42GXi1EYuoiZmTnVKpBXVV1rU3qczlY/azWNnNDVEmH299gxTSgU2Ds6I4DCSqAktcX1MDdrbov04oeDGSQXXPn/rygznfrqKgIjo7o04H4iu4IGja5zYSg8Db8+AG59YfKnYHiwVDVWcabsDAOcL24sVRt/FOs172ItryL+tAlNja2D4clkcpx8/Oh/483MfHUBZlZqVs+fR3HqaeZNDCEpv4plZ01gyhfwRDz0vYNje8vJy6gnOuAgVlKO/kYN1bD2Eb1tLr3h4X0Qou8hfrw9GaVc8Mio9hc3SpLE1q8+RavRMPmpZ7CyM+PknuxO200hlxHpa0e6qQe5FQKdqQpLXyUpeWY4eVle9uJDfT0ewq06CYfiBAo9xlMSH4NSZYrvIMOAeW3JBZsdztUR1WhKnc9AHln8LRMefQqNRsOmjxYi/z6OUZW9eOvgfM7lJvHzJ2/QpNAx/Ew+cyrCOh73qC/HTFQwYmgZkiQR+0cGWDggnMK4p1FOemU6p3/6EqFSoZ4wAQBzaxuqR9yJqa4Bx8Mr0DRdnegLRmG4DKrN5JjU1l5rM3qczoQhLWMXNTJBhFP7M1yuNma+vlhpdRTUW0JWTKvr/x0fjKVKwWsbT3U5btKlZJ1K0Jfl17Vd4mIM0TsH+XQgDLWlsHwWmFjArGWgvPj2fazwGDpJR39nvbtKV1tL7vPPY+LizKiHIqkuayB+S0aHNqgdnJj56gIs7exZ8/YrhEgFDAuw5/2tyZTWNIKtDyX93ySm5i78XAsIKv0APhsIq++Fr0fC8eUw8jmYswls9GEZUgqqWH88lzlDfXCyaj/sRML2zaQfiyP6jnuw9/AgdLgbWafLKC/o/DsU5WfPCZzRaXVkBT9OvYsVNaZOBDhfQYTjbS8jlaRTcsYRdcFOtApIPXIQvwGDUdoYXm5qipEkif2rU0nYnkWVuyk/aKpo0EFo9HXc/f7n3PTvZ5HL5fjsq2PcdjXfvvofpNoGbOaMwdrXj8IFC9HVdxBPzSA+aidrQoe5cXp/LpXFdeAbzbisU3iqXJBt34fV2LHIrfQDzI0aHb+c05Hfdyol55LZvvjzK/78Xg5GYbgMaswUmNb8P4mw2oEwJKRtBSDC7+qvyGwPla8P6qpaChrUSBkHW123szDhv+OD2J9awpZT+VdUxsn4vVRZaHnx+OtdSn84rZQgZ0vsLNqJt6RtgpV3QWWeXhTULQUnvjAehVAQ4RABQOF779GUlYXrgrfxiHAlcJAzR7dk6h8uHWBpZ8/MV97GxsmFdQtf55EADdUNGt7begatRsf27xNRmSsY9dQ0xJMnIOoxOLMZGmtgzkYYPRfkF8dIPtqegrlSzkPR7fcWyvPz2P3jErwj+tF3nD7EQ+hwN2Qywam9OZ22XZS/PbmmrgiFkvRKC8653IlCU4ttzM+d5m1B6naIXUKF/CYacwr4wXcYJgEV6LR1yE2C9fUys0WqKWbvimSO79CvYr5udjC1TVo2n9R/VmQyOb2GRnPXO58y+ekXsbN2wrZCgd2UoTx4wzM4v/QSTTk5lHzbwdYyBmHA3J4BE3wQQhD7ezr4jkChqeNfBT6Y1WopGR1xIcuWU/kUVzdy85QJRN16G6d2bycv5czltcEV0C3CIIS4QQhxRgiRKoR4vo3rHwohjhmOZCFEebNr2mbXNnSHPT1FrZkK87rOg4390+lUGIoSUOskvL1GtJvmamPi44O6po46jYLqs3Ftprk90oteLla8sek09U2Xtx/BkdzD5CWdJte+jhPFJyit73ijQa1OIi6jrOPewuYXIH0vTPoYPFqHFYkriCPUIRRzpTnV+/dTtmw5dnfdhUWk3i8+9BZ/hAz2r0ltlfdSLGxsmf7yW9i6exC7+D3u9axl+eFMfl+RRHFWNaPu6IWZlQlYOsH4N+C/SfBYLPi2/B+fyq3gtxN53Dvct13B0+m0bP7yQ2RyOdc//O8LbjcLaxW+fR04fTAPTRsusOaEuqqxMjel3sGXtGPxnCv1x6fxIHX7DqAp7uLMwLoyWP8Ykl0wxXvyqfcL4qBrGLaaVOQKM84lmFGYUYlk5siu+EBO7M6h3zgvhs8IZJCPHd725qyJb+n6EjIZgYOiePzDpdz+6Rc8MPMlfd0GR2I14QZKvl5MU047wtdMGCxtVYRHu5N0KJ9y8wEgZITvPUeZlWCp+dELWX46lIGHrRnRQY5ETbuNWa+/2+4akO7kLwuDEEIOfA5MAEKB24QQLXZtkSTpSUmS+kqS1Bf4FPi12eW689ckSbq6mwZfJvXmpljWdz0k8j+VzvZkOF5fQITM8povbGuOia8v6lq9aBfmFuhdNJegkMt4dXIYOeV1fLX7XJfvHZMXw7xf/4NSI2N8tH6z+EO5hzrMczqvkuoGTfvjC7HfwpHFMPRx6Nt6Xn+9pp4TxScY4DwAbWUleXNfxMTfH8cnLw5MW9qaMmCCD+eOFpF1uvMdcc3V1kyfNx8HL1/M9v7IiLpMMvbl0yvKBb++LaetYqoGVev9kz/cloLaVMH9I/zaLSfut/XkJCVy3T0PYWXfcme/8Gh3Gmo0pMZ3/HCXyQRD/Ow4o3CjoiAXTWM5fSYGgQ4qP36q07oC8PszUFNEucl0mnJy2R99KzYmguJTsQRFDcXC2pzt3yWyo+BOEnODGHijD1G3+COEQAjBtP4eHDhbQnZZa9eXEAJXp5bbxzo/8wwABe++17Y9zYQBoP8N3sgVgiPbS9Cow6k7lUv5qL5sy9pBZmUmKQVVxKSVcvtgL+QygZDJcA8O6Vrd/yLd8c2OBFIlSTonSVIjsALoKHj4bcDybij3qtNoYYFlXffvvvV3Q25tjVRf36a/tLoqj7NCS4T1tYuo2hYmvr6o6xsA9OMM2UfaTDfEz56JEa58sSuV/IrO99fYn7OfR3c8SmCl/gF3/fCZqE3UHMg90GG+8+ML54WhqaCA9NmzSZt2K2mTbiDtibdI2+1H2len9ecuOVJvmcobS+oZ+9oW0qbegqa4GLcFC5CZtvTp9x3ridrBlL0rU9BqO/9smllaMX3emzj7BtAn73eqG09THty1WDzHs8rZfrqAB0b4YW3WerqoprGRY1t+Y/+KHwgYFEXIiNabN7kH22LjbM7J3Z27k4b6O3BCp19Yam1fjMuMOahcLanYcQiy2v7/XuDUWjixCl3UUxQv/w2zfv1Yp/Qk2qKExro6QkeMZMxdIZTl13KmJJxIp20MnuzXYvB4aj/97Ke18Z3bCvqtZu0ffICqzZupOdR6nOtSYTBXmxAx2oPkIwVk5oaCBP3u+BcKmYIfEn/g55hMlHLBjIFXN+Q2dI8wuAPN40RkG861QgjhDfgCO5udNhVCxAohDgkhprSVz5D3QUO62KKiom4w+/LRWVlh3gjV/+NbfHa0yO1k8gYkIejj3vE+tVcbua0tJlZq1CYqCuut2hyAPs/T44Np0OhYf6zjL/yurF08vvNxfK19GaoJwcHLBytbe4a4DuFg7sEOBwEPp5XgZWd+YSpn9Z+7qIuNQ26pQlF/DoWVKYqA/igcHFE4OLQ6yi2h3EJg6eKJKiAAt7fmY9Y7vFU5CqWc4dMDKcur4eSurj3AVOYWuIXMQSjcUNRs5pdV67u0OdD725KxNVdyz3DfFuebGuqJ+20d3zxxPzu+/RKXgCDGPfhYmzN0hBCER7tTkFZJUVbHM8SG+ttjK+xBWKI00dfN5o77qS9V0rDkQf0q8baoKoBNT4Fbf8pz3NAUFGDy0L9ILarBryoFMys1XuF98Ay1Y8TMIEb1PsYgq1WtbuNpZ06Unz1r4rO7POBrf++9KN3dKZg/H0lzSZvWFIPCDEwuLgbsN84bpUrO0fwITO0acbVqYpL/JNalrmPN0SQmhLviYKnqUtndSXcIQ1vzs9prxVnAakmSmjsYvSRJGgjcDnwkhGjzVVSSpK8lSRooSdJAR0fHtpL0OMJaP289K7vrboh/Ih0JQ0LOPgDCg/5eXj8hBCofH2w0EgVNNpDZvjD4OFjQx9OGjQm57abZlrGNJ/98kmDbYL4a/SWFySl4hekHBYe6DaWwrpCz5WfbzCtJEkfSy1q4keoSEpDb2uA5OA3PMfV4/rwOz8VL8PxqUZvH0rs9WPdQKH5ff4PnV4uwvrn9TrhPhANeoXYc3pR2IThfR2QllZK4r5j+Nz2OXWAokdlb+XxRx4O6seml7Eku4uGR/hcW6zXW1XJ4/WoWP3Yfu374Bjs3D6bPe4uZry5sc5vK8wQPcUGhlHFyT8dCFuBkSaROhVD5UJJ1Gp1Oi3rqrSCXUX6sGLa/2jqTJMHGJ6CpFt0NH1O8eAnmQ4aQ4OCPQteElHGKoCHDkRlC50eM9iCsV43e9ahr3eOaNsCD9JJa4jLKOrT1PDJTU5yef46GlBTKVvzS8mJtKVi0dK2ZWioJizAl3yIEXYAzpO1hTugcGrQNNFjsY/aQzhcE9gTdIQzZQPO+jgfQ3jduFpe4kSRJyjX8PAfsAv4+cyAvQWmr7wIW5GR2kvKfTYfCUJ6Kn1agtr763dvOMPH1xaq8kuoGGbUZx/WzftphUoQrJ3MqSSuuaXXt93O/88zuZwh3COfr8V9Tm5mPpqkRT4MwRLlFAbTrTjpbVE1pTSORzQae644dw8xRQhSdhlu/A4f2Vws36Zo4XnS8xfqFjhBCMHxGIJoGLTHr2xar8zTUNrFz6WlsnM0ZfmsId738Bg0uQSgOrGL76jXt5nt/azIOliruivKhvqaag2uWs/ix+9i77Hucff2Z9do7zHj5LbzCIzqNYWRqoSRwkDPJhwtorGu/p1JVUo97vSDb0ouG2hoKzqaisLfHcuQoKnLskQ59Bed2tcx09EdI3gxjX6V0SwzakhIcn3iCmLRSghoy0TU1Xoh+ewELB5C0UN/68z4h3AVzE3mrQeiOsBo7FouhURR98gmasmaCUlvSZowqz9xdKDS1JDnfBWl78LX2xaypN2b2hwj3aH86cE/SHcJwBAgUQvgKIUzQP/xbzS4SQgQDtsDBZudshRAqw+8OwDAgsRts6hHM7fX+zrKCrnXZ/6lcFIaWA9CSTkeCtooI0yvbQL6nMfH1xbJQ78ctrJZDfvvbMt4UoZ+/vul4y3eY9anreWHfC/R16suicYuwMrEi89RxhJDhEap35bhZuuGj9uFAXtvCcOn4graqisa0c5iaZMH4+RAwpsN6nC45TZ2mrsvCAGDrYkHEGE8SD+RRmFHZbrq9K1OoqWhk7N2hKEzkKExMuO+V18m09OX4qu+I3bS2VZ4DqcUcPFfCw4Odift1GYsfvZcDK3/GvVcot89/n2lzX8e9V2gbpbVP+Eh3NA1azsS0P3X41N4chIDdpk4gBOkJ8QBYT52CtqqBmlpfWPco1Bs+p2Xp+plePiPQht5B6TdLsIgegXn/fhxOK6W/Jh1LW7vWtpob3uINq5+bY6FSMCHclU3H87o8k00IgfPcuehqaij66OOLF5pFVj2P1NhI/W/rCVBlkF4VREF6FSfOZVOSE4VOVs3Gsxu7VGZ385eFQZIkDfAYsAU4DayUJOmUEOJ1IURzf8NtwAqppbMuBIgVQhwH/gQWSJL0txUGayf9w6S6qOAaW9KzXNyToeUbVHbOQcpkggiH3tfCrE7RB9PTu1LaW+h2HldrMyJ97Fq4k1Ynr2be/nlEukTy5dgvsVDqB2UzTybg5OuPqcXFWTpD3YYSlx9Hg7ah1b0Pp5XiZKXC217vS67f+BVIYBY5AoY80mk94gr0023PL2zrKoNu9MHMyoQ9K5KRdK29ueeOFnHmUD4DbvDG2ffi6mEXO0vC73yCFAt/dv+4hJi1Ky9ckySJT36LZ1z1YWp+fI2YdSvxiejHnQs/Ycoz83ANuLLouk7eapy8rTi5J6dN/722SUfi/jyce9lSojRD4eRJRoJ+GqfVyJHIbWwor+oLVbn6eE86nV4kEDDlC0p//BFtRQWOT/ybyvomUrOLsC09S1DUiNaz6SwMD+va1sIAMG2AO1UNmsta/6IKCMBu9h2Ur1xJfaLhkdaGMFTv3Yu2rIx+U0IxNYPDVTM4svs3VNoAQu3C+SHxB7S6y5ta3R10y3xDSZJ+lyQpSJIkf0mS5hvOvSxJ0oZmaV6VJOn5S/IdkCSptyRJfQw/l3SHPT2Fk5t+elpDWdsfoP8V2nMlJZzdAkCET8dvvNcKla8vSp0OtaWaQq1Th8IAMKmPK8kF1ZzJr2J50nJeO/gaw9yH8dmYzzBT6AeNm+rryUs5g1d4RIu8Q92GUq+t52jh0RbnJUnicFopkb52epdKXgJ1Gz4DwOz+i+EuOiKuIA4ftQ8OZg6dpm2OiZmCoVP9KUir5Mzhlg+x2spGdi1LwtHLioE3+rTKe/cIf86GTSHbLoR9K37gwKqfqSot5qePPqbfwc/pVRxP4KAh3P3e50x66gWcfNqfrtpVwqLdKc2tIS+19dTos0cLqa9uYtBYL9ysTSlSe5ObnERDbS3CxAT1xIlUHzyKtv9jcHwZrLwTMvbBhAVoUVP63fdYjRuLWXgYcRll+NakgU7b2o0EHfYYAIb42uNuY8bquK67kwAcHn0Uua0t+W/O14tfG8JQvnYtcgcHbEePoN94bzIb+6NLPsPNfT24r/c9ZFZl8mfWn5dVbnfw95mI/g/A00s/G0NX0bWBqH8qMpUKYWbWWhgK4zHTSfj7jr1GlnWM0tsbZDJsVWYUNFhB1uEO00/o7YpMwIIDX/FWzFuM9hzNx6M/RiW/OAsk50wiOq3mwsDzeQa5DEIhU7QaZ8guqyOvop7BvnZQXQQrbqeu1AwTHy/kdp1PmtBJOuIL4y/LjdSc4MEuOPuqOfjr2Qv+e0mS2PVzEo11WsbcHdLmxkRKuYyXJ4ezTh2NPDiSg6uXs/jRe8mP2UmOXS/ufO9Lbnz8aew9vFrlvVL0UVIVbQ5Cn9yTg9rRDK8QO6L8HTjS5Iik010IS2I9ZQpSYyOVlcH6OE5JmyD4Ruh7ByXffoeupgaHxx4H9D244JqzWDk44RIQ1NqQ8wPC7fQYZDLBtP7u7E8t7tIU5/PI1WqcnnqSuvh4Kjesh4bKiyIEaMrKqN69B+tJkxAKBb3H+KCSV2NW04vZQ7wY4zUGD0sPvjv13VUJg9EcozBcBrY29jTKQVR2vu3lP522Vj8n1OQQLkxRKK/NgFhnyExMULq7Y92kpaJGR31ZAZS3v+Oeg6UK/8DDxFYtZZz3ON4f9T4m8pareTNPJSCTy3Hv1XJDeXOlOX0d+3Iwt2X4jcPn4yN5WcIvs5Gqi6mrVGPWt2tuoZSyFKoaq65YGIRMMGJmELWVjfpwC8CZQ/mkHS9m8M1+2Lu1XrR2nhGBjowLc+Ur3SDCJkzFdsBIfnC/jeH3PoaTR/dvZ6s0kRM8xIWz8YUtZlOV5FSTl1pB+Ah3hEwQ5W9PCg7ITVSkG9xJpmGhqAIDqdiwCaYtgT63waSP0ZSWUvrTT6gnTMA0WC8C8Wey8azLImRYdNsD4//H3pnHR1lf+//9nX2SSTLJZCEBQsK+JiirKIobaovigku1V2hrra16bXvpT3trvVZtL7eX2va2LrXu1gpWK+LSWvcNEAImIWyyJISQfbInM8ks398fz0zIvs1MZiY879crL2b5Ps+cAfKc53zPOZ/jv4tv7V/59ZoFE/BK+PuXw4saEq6+GtPcuVRv/A1el+iWfG568y1wuTrHd+r0Grzxh2l0TSOhpgGtRsvNc26msKaQ/Jr8YX1uoKiOYRhotVpazAJ969juY4De3c9ORz0HhYucuKzwGTUEDFlZWOyKQ6t2xva7nSSl5NH8R6nU/h1X43xunvwz9JreTVsn9hUybuoM9KbeznBZxjIO1h2k1nHqTnNncR0JJh0z8u6HEztwLXsQT0MT5tycXsf3hT+/MFLHAJCWFc+sZekUfHCCE/vr+HTzV2RMs5J74eCVZD9fNRsP8KbhDDbrFpGcns7VZwxf5nqozD13PF6P5MC2U7meok9OotVpmLVMyemdNcWGV2gRGdM47ktACyFIuPJKHAUFtDcb4KrHwZKK/c9PIp1Oku+4AwBHhwfH4S8RSGb0tY0EoDOCMb7fiAFgki2WRVmJvLp76D0NoEhopP3sP3HX1FK739JtK6nxtdcwzZ7d6cC2HbXzrtRg0dTyxZavkFJy5dQrsRqtPFP0zJA/MxiojmGYtJh0GE8HIb2EhG4Rw8Ejb+MWgpz0xWG0anAM2VnElCp3dfr19VsAACAASURBVNWuxD63k6SU/H7P73ms4DEuy7ocT9X1vF3UW6LBXyLZM7/gZ1mG0uS3o+KUPMbOkjruTvoE8eULsHw9DocijGfOzR2S/burdpMem06GZWgKrv2x9Mop6PQa3vhDPlLChWtnodEMnt+YmBTDbedO5s3CCg5WNvPDi6ah04buMpE4LpbxM6zs+7Qcr1fS4XRz6ItKpi5IxWRRHPV4q5ksWwxlMRNoqKygoUrJn8Rfvgq0Whq3bAHAVVVN/UsvkXDFFRgnK9u+X56oZ3LzYYzJ40iZlN23EaBcsPvJMfhZs2ACR2tayT8xvJksMWecQcKFS6k7ZKGjQUkkOw99hXP//s5oARRdpGLzdBYkbKWyQkvpvjrMOjM3zLyBj058RHFj8bA+NxBUxzBMWmP0mB29K1HGGj23kgpKPwYgJwImtg2EMTsbQ0srFmsSVUyEE701jfJr8nmq6CmumXYNG859iHOnpfFmgXJh6krZgSKk9PbKL/iZmTQTq9HauZ1U3ewko24HN9Q/BjO+Duf/DEdhAcJkwji9j73tHkgp2V21e9jVSH0RE29g8eWTkRLOXjOV+OT+h+n05PsrpjLeamZGWhyrcgJzUENh7rkTaLY7Kd1n56udVbicHub2mCB31hQb2xzK/ry/OkmfmkrsOWfT+PrrSI8H+5/+hPR4SL79B53HfbGvhAnOcuacc97A/RWxydA6sKLC1+alY9JrhtXT4CflhvMQGknVk0o5cOOWLaDXK84NqGpy8q/9VVy9aDKzZjqJ09ex841jSCm5YcYNGLQGnt///LA/d6SojmGYOMwmYoMwIjLS0Vq7RwyF9QcY74HklNER8RophmzlrjA5KZlqhwkqi5RBLV0oa1Z+sdfNWYdGaLg8N4PyRid7SrsXFZQWFaLTG0if1reapVaj5az0s9hWvg0pJfv25vOI/v9oT5gCV/8JNBqcBYWY5sxB6AYf7VnaXIrdaQ9oG6krORdM4Mb7lzBn+fC2gswGLW/ceQ6bv7cU7RCijEDJnp9MTLyBfZ+cpOiTk9gmWLqV0wKcNSWZk9KCyWrrdAwA1iuvxF1VRcPf/0793/6G9ZprMEw8tWVWsmsbAshZvmJgI2KST2kZ9UOcSc+lc8axNb982Oq8eqOL5DnNtGzfTfMHH9L4xhtYzjsXXaKiprBp5wk8XsmNizPRTjmHReYXqT7eTHFBLTazjSumXMHWI1u7bVuGEtUxDJP22BgsjtGvKx5t/DkG6ZMJKOyoJ8cQ/jGeg+F3DIkGI3WNTjo8Ek52l+G2O5QLgM2s7PdeNDsNo07Dm4UV3dadKCogY8YsdIZ+5imgdEHXOmo5XJXPnE++Bwh039wMxji8HR04DxzAnDN6+YWuCCFIHDc0gbyeJMUasMb0/72DiVarYfY5GZTstWMva2HuueN73d2fNdkGQuBKn05pUQFej/I7aLngAjTx8VT+4gEEkHzb9zqP6XB7MZYW4klMxzZhkPxK7OBbSaAkoZucbt4/METpbz9tdSRNb8WQNYny9evx1NZiveoqANweLy/tLGX5tGSykmMh+1xmmD8iIcGjRA1eyc2zb8bldfHSwdHRH1UdwzDxWCxYnBKPZ2w7B63VCl4v3uZmqqoKqdQKcmyRHS0A6FJT0cTEEO90gYQap6VXnsHutGPQGLDolQodi1HHBTNTebOwAo9vO6mtqZGa0hIy5w6cG/DLY2x/dz02ZymPpt6HPlmp8W8/eBDZ0TGs/EKSKYns+AH2wscos8/JQAjQm7RMX5zW6/2UOCPTUi0cNYynva2VyqPK/G6N0Uj81y4DtxvrDTegTz81bnZX0VHSnJWkz186uAH+iGGQxPKyKcmkJ5h4ZXf/1W590mZHxCYoHdFtbWgTE7EsV2ZdvH+wmsom5yldpPRcNKY4Fmd9if1kK0f2VJOVkMX5E89n86HNtLlCP0VSdQzDRMYnoPNCdXXF4IujGG2Cr8mtsZG9h98CICezt4xypCGEwJCVRVyNUjZapZvSqzLJ7rBjM9u63ZVenptBbUs7XxxToomy/YqcxsQ5A3d5j4sdxxRdHNtajnO/ay1xMy/ofM9R4BsFOoyKpAVpCwbVGhqLxCWZOPPSSSy5fDIGU9/bbsum2Pi0xarIYxSc2k5KuvlmYs85h+Tv3dpt/e4Plcaw5ZcMoe8mNhm8rlPyGv2g1QiuOmM8nxyupbpp6D0N/uY2y7nnkrT2ZpLvvAPhi0T/suM46QkmLpzpk5rRaCHrbKa2v0Rieiy73izG65V8a+63SDQmcrIl9JI8qmMYJtoEZTvlZFlJeA0JMV1lMQord6KXkplTvxZmq4aGITsbzfFSYhKsVMtxULazm3Km3WknydR9W+z8GanEGrSdEhmlRYXoTWbSJk8b+MMK/8ZZ9pPkxcTygve8XoqqupQUdOPGDWpzZWslJ1tOcmZq4InnaGXp6ikDltSeNSWZBq+euAnZ3fIMxsmTyXzyz+iSu3eKN+3fRUNsOlnZQ1Ao9TeeDZJnAGU7yeOVbBlEtr0bbbWdpappP/0pSTfeCEBJbSufHq7lhkWZ3au/ss9F03CMxRfEUV/ZxuGdlcxPnc/rV77OtMRB/k8GAdUxDBOTTelerakYZigZZXSVxShoKWWWNGAwxoXZqqFhyM7GXV5O6qRsqpt1yl1g7ak5uXXOus78gh+zQctFs9P4R1ElLo+X0n2FTJg1B+1ASeOTu2HrHSyLm0wHXoyWUnInWjvfdhQUYJ6fO6QIINj5hbHI0slJCAGtyVOoOHII5wD9RLUnyzA3VaCfPkRHGzuwLEZXpqRYOCPTyqu7+9Z56pM2e7euZz9/3VmKViO4YXEPh5ilbDNNseSTPNHCzrdK8Hi8aMToXLJVxzBM4lKUu7/Gsb6V5HMMHbXV7Jft5FiC3/kaKgzZWSAlNquNWnsTbq/otp1kd9ixmWy9jrs8J4OGNhcf7DlMfXlZv2WqADRXwqabIDaVBVc/D1LHuLTjmPSKzr+7vh5XaSmmYSSeLXoL0xMHL2s9XbHGGJidHs8+Ma6bPEZffP6v95DAnLOHOJc8ZmAhvZ5cu2Aih6qaWfvMLvJKBh+rSltdL50kp8vD3/JOsHJ2GmnxPRooU2dDjA1R8gmLL59MU42DQzuGLuIXKKpjGCa2cUrpn8M+zKqEKMPvGCqPfoFTI8iNojtZo78ySatHer3UivGdg3u80ttnxACwfHoy8SYdn3ys9CVM7C/x7HLCphvB2QTfeAlpSsfTNglp/qpzibPQl1/IGXri+YzUM9BqtEP+nqcjZ0228VljLHqTqdt2Uk+O7fyccmM6Z80b4gjaYUQMANcvmsjdl86k6GQjax7fzjee2MG2o7V9RxCdAnrdty/f3ltBfZur72E8Go0SNRR/StbcJFKz4tn1VjEe1+iMFlYdwzAZN175R3TVD74XGc1o4+NBCKrLFMngeVMuC7NFQ8cwSfk3SvA1IlYZZ3VGDI3tjXikp8+IwajTcunccdR+tQ9jrIXUvjplpYQ37lK2ka56HMbN5cvSBtyt06h3H6emTWmSchQUgEaDee6c3ufoQZ2zjmONx9RtpCGwbKoNp1dgyZrVqZvUk9rSErx1FdSmziLDOsTGvpiBhfR6otUIvr9iCp/dfT73fn0WR2pauPHPX3Dt49v56FB1dwfhagO3s1fE8Jcdx5mcHMuyKb3/LwKQvRyayhD1xSy5PJuWunb2f97/1MFgojqGYTJhfBZegKbhtcVHG0KrRRMfT0NdJTaPJCN9YbhNGjKa2Fh0aWnoKyoxxVqodtug7ii01vbqYejJqpwM0lrLMEyY1lu3H2DbH6BwE6z4T5itjBvZWVKHt1VJCPrlMRwFhRinTUMTO3gfwZdVygVOdQyDsygrCa1GUGfNorGqkobK3lu6B7d9ghdBWs6SoZ/YEAP6mAGF9PoixqDjluWT+fT/nc8Dq+dQ3uBg3TO7uPKRz3l3f5XiIPxRSBfHsK+8kT2lDdy4JLP/HFT2ecqfxZ8wcXYS6VMT2P2PEtwdoS+VVx3DMDEajbSZQNs88CDzsYDWmoCj2UGOLqHvi2QEY8jOpqPkOKnZU6hq9IXfJ77A7lR+8XtWJfmZE+ciwd1MsaEPKYiv/gXv3gezV8N5/6/z5Z3FdmbaZpJkSlK6oL1eHHv3DrmxLa8qD6PWyBzb4NHF6U6cSc+88Qns8SilnT2jBiklRZ9+TJlpPItmDVMiPCZ5yBFDT0x6LTeflcVHPzmfDVfPo77NxXefz+Oy33/KZ4W+wocu857/sqMUo07DmgUD5O5sUyEuHUo+RQjBksuV/piG6ijpYxBCXCqEOCSEOCKEuKeP99cJIWqEEPm+n1u6vLdWCHHY97M2GPaEmmaTFkNb6P9xwo7FjHBCziiUxwUbQ3YWHcXFpGZPobayBo8wwIkvqHMqicK+tpIAyg8UAfBRUwJtHV3mEdccgle/A+PmwpWPdQ7caXd7+LK0gSXZySxNX8r28u20lxTjbRqeompuSi56bW91V5XeLJtiY6ddQ1xyaqfaqp+qY0dora3isGVqt9LhITHE7ueBMOg03LA4kw/+4zx+c20uHW4vf34nD4CPy7y4PV6anS5ezz/J5bkZA3eXC+HLM3wCUjJ+RiL/9tAykieEvjowYMcghNACjwCXAbOBbwgh+hoAu1lKOd/386Tv2CTgv4AlwGLgv4QQiYHaFGrazDpMp4HCaqvOQZxTkjthiJUdEYQxOxtvczPJKWl43G7s8fOh9ItBt5JKiwrQWxKoEPG855c9aKuDl25Q5JlveAkMp7aH9pY10u72sjg7iWUZy7A77ZRs+xcwNEXV5o5mDtUfUreRhsGyKcm4JRizZlFaVIjHfcqBH9r+KVJoaUqd2TladcgEEDH0RKfVcM2CCbz74/O46yzFQf3XexVc9PDH/OdrRbR1ePpOOvck+1xF3K/mIABaffSUqy4Gjkgpj0kpO4BNwOohHnsJ8K6Usk5KWQ+8C1waBJtCSpvZSIyjY/CFUY5d04zFAXMiXFG1Lzo1k4RS5VOtmwrlX2Jvq0YrtCQYE3odI6XkxL5CJufkkpZg4o2CcvC44ZVvKQN/rv8LWLvXm3/hH8yTldQpj1Gx6xM0sbEYJg8+/jK/Oh+v9KqOYRgsmJSIXiuosGTS4Wij8ohSDSa9Xg5t+4SKuEzmT8sYfgd5bPKwcwyDodUIzkxWtjLvvXY5sUYdbxSUM3d8PLkTev8f7EW2b4ZE8adBtWswguEYxgNdu73KfK/15BohRKEQ4hUhhP+3a6jHIoS4VQiRJ4TIq6kZWB431DhjTFic7sEXRjmV2hbiHRBjSQ23KcPG7xhMDU3oTWaq2uPB04697jBJpqQ+G4XqTpbR2lBP5txcvj4vg48P1dD+9k/h2Eew6reQ2VtzZ1dJHdPTLCTFGkiNSWWqdSpi32FM8+YhtIOXnu6p3oNO6MhJGdq2k4rSjHhGZiJfOG0IoenMM5R/dZBmey17jZOV0arDJcamRAzBHqPZVgtCy0Xzp/Hmnefw1+8u4ZEbzxya40qcBNZMKP44uDYNQjAcQ1/fruff7BtAlpQyB3gPeG4YxyovSvmElHKhlHJhSsrgs3NDSUdsLBbH6NQThwuvx02JyY2pA2RH9EVH+vR0hMGAq+Q4qVmTqa5TdG3sjaX9byPtKwAgc04Ol+emcxXvY9z9BCz9AZz5b73We7ySvJJ6FmWdugidY1tM8slW9PP62k3tze6q3cxOno1ZN/R5CSpKniG/up2UyVM78wwHt32C0OkpjslicXY/JaADEZuslJV2tAbXWJ9OEhoNQgiWTUlmkm0YqrfZ50LJZ91kXUJNMBxDGdA1vp4AdCu2lVLapZT+6TZ/BhYM9dhIxBsXR0wHtLSM3RGfJaUfY49R/HbXEZ/RgtBqMUyaREdxMWnZU6guK8ObMAm7o6bfiqQT+wqJS04hIW0c8737eUj/DIWmhXDxg32uP1DRREu7u1uS85yWdLQSTmQOfqF3up3srd2rbiONgLMm25AS5PgZVB45TFtTI1/t+AxH2nRiLbFMS+1/tnW/DLOXYcj4HcNIyT4PnA1QtTd4Ng1CMBzDLmCaECJbCGEAbgC2dl0ghEjv8vQK4IDv8TvASiFEoi/pvNL3WkQjEpT8+ImyY2G2JHQUHHuHZt+1revAnmjCkJ3dWZnkbm+n3rqAOncbtj4cgyKxsJfMObmIxhOIl2+myZTBzU23Uefs+07Nn1/o6hgmlSnR1fbEwfeq99buxe11szAtenpEIoX5mVZMeg3HjROQ0su2v/2VtsYGioyTWZSVNKQxpr3o7H4OcvNqH3IYw8Knm0TxJ8GxZwgE7BiklG7gDpQL+gHgZSnlPiHEA0KIK3zL/l0IsU8IUQD8O7DOd2wd8CCKc9kFPOB7LaLRJyr/yFUnS8NsSegorCnA45NvicaIAcCQlUVHWRmpE5Xqj0oxEbsAm+hdFlpTWoKzpZnMmdPhpW+Ax0XdFc/R4I3hH0V962LtLLaTmRRDesKp6MBddIDGJCMft+YPal9eVR4CwfzU+SP8hqcvRp2WRVlJfN4Yg8EcQ8G7b6MzmtjlSWNx1ggHSoU0YghgyFV8OtimjWoCOii1T1LKt6WU06WUU6SUv/S9dp+Ucqvv8U+llHOklLlSyvOllAe7HPu0lHKq7+eZYNgTamJsyiCR+qrQ66KHi0JHJRkxygUvmiMG3G4sHtDpDZxs1dOhEdgcTb3WlhYp+YWJx1+A6v1w7dNMnXUGk1NileqkHkgp2VVS36tW3lFQgGtmNkcajlDVWjWgfXuq9jA9cTrxhvgB16n0zdLJNg7WtJE2cy5IiXlqLm6Nfvj9C35ifXf1AfYy9KK1NrCIARR5jOOfg2d0xgpHVztrhJCQquyMtdQM/IsfrbS1VHNEeMhKUe60o9UxGLOzAHCfKCVlUjYVlb4ehqbeKpUn9hWSmGAi7vjbsPIhmHoRQgguz8ngi+I6qnoMZTla00Jda0e3u1NXdTXuigpSFi4DYHvF9n5tc3ldFNQUqPmFAPBrDLWnTQWgwjaDGIOWORkjdLShiBi8XnDUdet6HhHZ50JHC5QPHokGA9UxjIDUDKXVvr1+dAZzjzb7Dr+BVwimT1UucNHqGPwlq/48Q33pCZBgqy3uts7r8VBWlM9EUQzzb1KqkHxcnpuOlIoSZlf6yi/4FVUnLr0Am8nGtvJt/dp2wH4Ah9uhOoYAmDc+AYtRx4G4mVx2x3+wvSONBZMSuw+8GQ7GONAaghsxOBtAegOPGPx5hpLRyTOojmEETMxULjjexvowWxIaCsqUvcy5c69C6PVR6xi0CQlok5LoKCkhNXsKHmc7cW06bPZjimS2j6qd/6Cjw0VmZorSr9Clvnxqahyz0uN7bSftLK4jNc7YrbvWUVAAej3m2XM4K+MsdpTvwCv7Tlz7B/OcmXb6TmwLFJ1Ww+LsJLaXNJGx4GwOVrWMrH/BjxCnZj8HizZfyjRQxxCbDKlzRi0BrTqGEZBotdGhBdEUnUnZwSisP0yWBxKTJqO1WnFHqWMAJWpo95WsAtiaDCR53HBS0a+hpZrSV34FwMRv/VGRvejBqpx09pQ2cKJO0ceSUrKzuI7F2UndmpQcBYWYZsxAYzKxLGMZ9e31HKw72Ot8oDiGrPgsks0BbjGc5iybYqO4trXTcY+of6ErQdBL6obfyQSSfPaTfS6U7gB3++BrA0R1DCNAq9XSYhboBxgtGK1Ir5dCdyM5JqWJUGtNwBulVUngF9MrwTZxEmgEtkYDiR6pDO5xt8Ommyht0JGcnkZMxtQ+z3F5jqK0+pZvO6ms3kFFo7Pb3an0eHAWFXUqqi5NV7qk+9pO8kove6r3qNtIQeAsX57h0Y+OYtBpyBmKzMRABFEvCTh1rkAjBlAcg5SKoGOIUR3DCGkx6TCOQSG98oo87FpBjm0uANoEK5766I0YjNnZeOx2hMOBx2YmrSUWXdocOLED3vwR7tJdlDsTyZzfv3Z/pi2G3InWzrvSnX59pC6Oof3IUbxtbZjnK8J5KTEpTE+czvby3gnow/WHae5oVh1DEJg1Lh5rjJ6KRifzJ1o7R6uOmNjkEEUMQYgMp14E9xyH9NDLp6iOYYS0xugxO0If0o02hUfeBiAn60IAtIlWPI3R6xi6JqAdNi2JjTrkhMVw7GPIf5GKGd/D7fb0P8bTx+U56ewrb+JYTQs7i+tIMOuZnnpK/thRoFSLdJ3BsCxjGXuq99Dm6i7R7s8vqI4hcDQawVLf9lFA+QU/Qc8x+B1DECIGnQH0oyOdojqGEeIwm4h1jE5N8WhSWLUbk1cybfIlANGfY8hSHEN7cTH1CW707dCcMA+QMOtySnXzEELDhFkDD8n5eo5SovxmYQU7S+p6ddc6CgvRJiSgn3RKSvmsjLNwe93kVeV1O9fuqt2kx6aTYeljGJDKsFk2VbnoLhppY1tXYm1KWajLOfjaodBmB51ZmRAXRaiOYYS0x8ZgcYR+xN5oU9haxhxhQqdX2p61Viuehsa+h5xHAYaJE0CrpaO4hAqLkhOq0k2GSzfAlY9zYv9eUrOnYIodWFsnPcHM4qwkXtpZSnFta6+7U2dBIabcnG7J6DNTz8SoNXbbTpJSsrtqtxotBJFrzpzAA6vncPbUIGzXBLuXIVA5jDChOoYR4rFYsDglHs/YcQ4d7c0cEC5y4k6NRNQmJIDLhYzSiXVCr8cwYQIdxcWUGu1IAdVlZbD0+7ikjorDh8icO7Q928tz06loVO4ku/YveFpaaD9yBHNO9+0ok87EgrQF3RLQpc2l2J12tUw1iMQaddx8Vhbakegj9TqZXy8pSI6htTY4FUmjjOoYRoiMT0DnhcoxJItx4PBbuIQgd9zizte0VisQvU1uoOQZnMeO0oIDbXI81cVHATh5aD9ej5vMOUNzDJfNS0cj6NVd6ywqUiQZ+hjluSxjGccaj1HZqnRbq/mFCCfoEYM98K7nMKA6hhGiTVDuAk6eOB5mS4JHcuJkfpCQQ+6MKztf8zuGqM4zZGfjKi1FeCUx41Op8jmG0n2FaLRaxs8cOL/gJ9li5NK547hwVlq37lpHgdLxbJ43r9cx/qlu/u2k3VW7STIlkR2fHdB3UgkRwVZYDVRyO0zowm1AtGKyKXX+tZUnBlkZPYwfv5jvj3+x22tjI2LIgg4XyU1aEidlciL/CC31dZwoKiB92gz0JtOQz9XX5C1HQQGGrKzOv6uuTLNOI9mczLbybVw17arO/MKwx06qjA7+i7iaY1AZCXEp4wBoqo74uUIBMRYcg9FXsppRJ0mfPB2AE/v3UnXsKBOHuI3kp+cFXUqJo7Cwz20k//plGcvYXrGd8pZyTracVLeRIhmTFYQ2ODkGjwvaG1XHcDphG6eMpnbYwzt/OtR0Ooao7n72OQY7ZE1VGvf2vLUFKb1Dzi/0h7u8HE9tLaac/s9zVsZZNLY38pcDfwGUaiWVCEWjOTX7OVCCKYcxygTFMQghLhVCHBJCHBFC3NPH+z8WQuwXQhQKId4XQkzq8p5HCJHv+9na89hIZdx45Su4GoI87SnC0MYrSdZojhi0NhvuGAMZdZLUxHQS08dTefQwOr2B9GkzAzq3w6eoas7tf9jOWelKnmHzwc1Y9BamJ04P6DNVQkxscnByDMHseh5lAnYMQggt8AhwGTAb+IYQouck9C+BhVLKHOAV4Ndd3nNIKef7fq4gSpgwPgsvQBTfSQ8FYTCgiY2NascghKBpXBwT67UYtUZSfYJ6GTNmoTMYAjq3I78AYTRimtH/xd5mtjEraRYd3g7OSD0DrSZA2QaV0BL0iOH03EpaDByRUh6TUnYAm4DVXRdIKT+UUvoL4XcAE4LwuWHFaDTSZgJtS3O4TQk5SpNb9DoGAHuKiQyfArJfaTVzEBmMoeAoLMQ0ezZC33tcaFf81UlqfiEKCJZe0mnuGMYDXUtzynyv9cd3gH90eW4SQuQJIXYIIa7s7yAhxK2+dXk1NZGxr99s0mJobQ23GSFnLDiGCpvA2ujG29bGxNnz0Gh1TD5zUUDnlB0dOPfvx5w7uIO5MPNCdBod54w/J6DPVBkFgqWwGsWOIRjlqn3V3fWpnyCE+CawEDivy8uZUspyIcRk4AMhxF4p5dFeJ5TyCeAJgIULF0aEPkOrWY+5LUiaKhGM1mqN6uQzwHGrG4COkhLGzZ7Nnc++HPA2kvPQV8j29n4rkrqSk5LD9m9sx6QbemmsSpiITQZno1JVpB04EhyQ1tM7+VwGTOzyfALQq4ZTCHER8DPgCillpyyplLLc9+cx4CPgjCDYNCq0xRiIcXSE24yQo01IiPqI4Ui8Etm1FytjPQN1CgCOwgKgu6LqQKhOIUro7GUIMAHdZgdTQmDOJUwEwzHsAqYJIbKFEAbgBqBbdZEQ4gzgTyhOobrL64lCCKPvcTJwNrA/CDaNCs4YM7EOd7jNCDl+Ib1opcPTwRFLG1JAR3FJ0M7rLCxEm5yMLkNVSR1TBEsvKUq7niEIW0lSSrcQ4g7gHUALPC2l3CeEeADIk1JuBf4XsAB/8zUIlfoqkGYBfxJCeFGc1AYpZdQ4BldsLHHOqnCbEXK0Vivepiakx4PQRl9FTZ2zDpde4Eqx0lFSErTzOgoKMefmql3MY41g6SWdzo4BQEr5NvB2j9fu6/L4on6O2wb0FpiJErxxcZg7oKWlBYtlYNnmaEZrtYKUeJqa0CUmhtucYWN3KlsC3onj6PBtJQWKp6GBjpISEq66KijnU4kgghkxxEdnNKl2PgeASFAukidKe+XKxxTaxOiWxbA7FMegy5pER3FxUGZLOPbuBRhS4lklyuiMGIKQY4jSiEF1DAFgSFT+0SvLS8NsSWjRJigD1qPdMcRMnoa3rQ13deDlzo6CQhAC09yoDXhV+iMmCRCBRQxSqo7hdCXWWJsKIgAAIABJREFUlgZA/RiaydAX0S6k599Ksk5TGvKDsZ3kKCjAOHUqWktswOdSiTA0WjAnBpZjcLWB26k6htORhDRl/7C1dmwnoE85huisTLI77Jh1ZuJ9ukgdJYE5BiklzkJllKfKGCXQ7ucobm4D1TEEREqGMgKzvS5I2u0RSrRHDHXOOpJMSejS0hBmc8ARg+v4cTyNjUPuX1CJQmKSA8sxqI7h9CVzgiLn7G2sC7MloUUTFwdabdQ6BrvTjs1sQ2g0GLKyOpvcRspQFFVVopxYW2ARQ6vqGE5bkmzJdOhANDeF25SQIoRAGx+PpzFKHYPDjs2k/IIas7MCbnJz5BegiYnBOHVK4MapRCaB6iX5I4YonPcMqmMImGaTBn3L6SKkF505hjpnHTaz4hgMWVm4Tp7E2zFyKRNHYSGmuXOjstlPZYjEJitjOb2ekR0fxUN6QHUMAdNi1mJsc4TbjJATrQqrbq+bemd9Z8RgyM4GrxfX8eMjOp/X6cR58OCQFFVVopiYZECCo35kx7fZlRGhxoSgmjVaqI4hQNrMBmLa2gdfGOVEq2NoaG9AIkkyKXduhiwlL9Q+QmkM5/4D4HarjW1jnUC7n9vsSrSgic5LbHRaHUE4YozEOlzhNiPkRKtj8De3dW4lZWcBIxfT8yuqDjTjWWUM0KmwOlLHUBu1iWcIklbS6Ux7bAwW5wjDzShCm5AQlTMZ/M1t/q0krcWCLiVlWCWrUkpcx4/TumsXjX9/DV1GOvrU1JDYqxIhBBwx1EXlrGc/qmMIEI/FgsUh8Xg8aMdwMlJrtSIdDrzt7WiMxnCbM2R6Rgyg5BkGcgzS66X98BHa8nbRlpdHW14enhrlAqFNSsL23e+G1miV8BOowmqbHZL7nwMe6aiOIUBknBWthPKKMiZOmBRuc0JG1yY3TVpamK0ZOnVOpcekp2No/uc/O59LtxvngYOdTsCRl9cZHenGjSN26VnELFxIzKKFGLKzVZnt0wH/NlDrCJvcolgnCVTHEDBaq5LUrCgrPm0cgz6KHIPdYUev0ROnj+t8zZCdhaexkZr/+wOOvXtx7NmD1ze7Wz8pE8tFFxKzcBExixaiHz9edQSnIzqDUlE0kojB6/VtJamO4bTFnJQCQE1lWZgtCS2djqE+uhLQnV3PXS7uphkzAKh99FGM06YSf8XlSkSwcBH6NDV3oOJjpN3PzgaQHtUxCCEuBX6PMsHtSSnlhh7vG4HngQWAHbheSlnie++nwHcAD/DvUsp3gmHTaBGXOg6ApuqKMFsSWjpnMkRZAtrutHeWqvqJWbqUSS/9FUNWVlQOHlIZJUba/dzmk8iJ0q5nCEK5qhBCCzwCXAbMBr4hhJjdY9l3gHop5VTgt8D/+I6djTIjeg5wKfCo73xRQ1LaBACc9sA1/iOZaJ3JUOeo66xI8iOEIOaMM1SnoDIwsckjyzFEedczBKePYTFwREp5TErZAWwCVvdYsxp4zvf4FeBCocT2q4FNUsp2KWUxcMR3vqghw5dXcDUEOO0pwolWhVW7w94t8ayiMmRibCOMGKJbQA+C4xjGAye6PC/zvdbnGimlG2gEbEM8FgAhxK1CiDwhRF5NTeTcnWdkZOIFaIquLZbhojGZECZTVDkGr/QqOkmm6P0FVQkjsT7p7eGOglUdAwB9lWz0/Jvsb81QjlVelPIJKeVCKeXClJSUYZoYOoxGI60mgbalJdymhJxo635uam/CLd1qxKAyMmKSwetWksnDwR9lnOaOoQyY2OX5BKC8vzVCCB2QANQN8diIp8WswdB6miisRlHyuWfXs4rKsOjsfh7mNnGbHXRmMETv2NdgOIZdwDQhRLYQwoCSTN7aY81WYK3v8RrgAyml9L1+gxDCKITIBqYBO4Ng06jSYtZjbnOG24yQo01IiKqIwd/clmSO3iSgShiJHWH3c5T3MEAQylWllG4hxB3AOyjlqk9LKfcJIR4A8qSUW4GngBeEEEdQIoUbfMfuE0K8DOwH3MDtUsoRCqCHD4fZQFzLaeAYrFbaDx8OtxlDplMOQ40YVEZCzAj1kvzKqlFMUPoYpJRvA2/3eO2+Lo+dwLX9HPtL4JfBsCNcOGPMjKtRcwyRRudWkppjUBkJI44YolsOA1TZ7aDgio0lzukNtxkhx59jkMOt0ggTdocdrdBiNVrDbYpKNDLSiKE1uiW3QXUMQcEbF4e5A1rGeGWS1moFjwdvlHxPu9NOoikRjVD/m6uMAL0JDJZT5adDpa0uqrueQXUMQUEkKB20J0qPhtmS0BJt3c92h13NL6gERsww9ZI8LmhvVCMGFTAkKv8JKstLw2xJaIm27uc6Z10vnSQVlWERO0y9JL9OUpQnn1XHEARibYoMdX3VyTBbElqizTGochgqAROTPLyIYQx0PYMqux0UEtIyAGitrQqzJaElmhyDlFKR3Fa3kkYdl8tFWVkZTucYKOGe+e/gbocDB4a23t0Ol7wM3tShHxMCTCYTEyZMQK/Xj+h41TEEgZSMTADa60Y4BjBK6JTejoKZDK2uVto97WrEEAbKysqIi4sjKysr+occNZ6E1hpInwlD+S6OeqgHUmaC3hxy8/pCSondbqesrIzs7OwRnUPdSgoCmROUv3xvY12YLQkt2jhlClo0yGKoPQzhw+l0YrPZot8pAGh1gAQ5xHJ0r1v5UxO+e24hBDabLaCITXUMQSDJlkyHDkRzU7hNCSlCp0MTHx8VW0lq13N4GRNOAU5d4P0X/MHw+oQbNOEdKxPo37/qGIJEs0mDvuU0EdKLAsfQqZOkViWpBMKwHYMbhBaivHcmuq2PIFrMWoxtjnCbEXKixTF0RgzqVpJKIPThGBoaGnj00Uc7n5eXl7NmzRrliccd9mghGKiOIUi0mQ3EtLWH24yQo7VGh8KqP8eQaFLHd6oEwBAcQ0ZGBq+88sqpdWHMLwSL6P8GEYIjxkha9djOMYASMXQcKw63GYNid9ixGq3oNSMr11MJDr94Yx/7y4P7ezE7I57/unxOv++XlJRw2WWXcc4557Bt2zbGjx/P66+/Tnl5Obfffjs1NTXExMTw5z//mZkzZ3L06FFuuukmPB4Pl112GQ8//DAtLS20tLSwevUV1FdX4PLCQ7/awOrVq7nnnns4evQo8+fP5+KLL+b2229n1apVFBUVsWTlNTz9hw3MSZkBwIoVK/jNb37DzJkzufPOO9m7dy9ut5v777+f1at7TkCOHNSIIUi0x8ZgcUadYviw0SZEyVaS2sNwWnP48GFuv/129u3bh9Vq5dVXX+XWW2/lD3/4A7t372bjxo384Ac/AOCuu+7irrvuYteuXWRkZHSew2Qy8drfX2PPOy/x4Rsv8x//8R9IKdmwYQNTpkwhPz+f//3f/+32uTesvoSXX/8nABUVFZSXl7NgwQJ++ctfcsEFF7Br1y4+/PBDfvKTn9AawcO91IghSHgsFiwOicfjQauN/j3G/tBaE/C2tCBdLsQIm2dGA7XrOTIY6M4+lGRnZzN//nwAFixYQElJCdu2bePaa0+p/7e3K1u/27dvZ8uWLQDceOONrF+/HlD6Af7zZz/jkw/+hUar4+TJk1RVDdzEet2qi7j4pjv4xa9/z8svv9z5ef/617/YunUrGzduBJSS3tLSUmbNmhXcLx4kVMcQJGScFa2E8ooyJk6YFG5zQkZn93NjI7rkyFWQrHPWMds2O9xmqIQJo9HY+Vir1VJVVYXVaiU/P3/I53jxxRepqalh93uvojfGkLXgwoF7A7wexqenYEtKorCwkM2bN/OnP/0JUJzMq6++yowZM0b8nUaTgLaShBBJQoh3hRCHfX/2yvQJIeYLIbYLIfYJIQqFENd3ee9ZIUSxECLf9zM/EHvCidaqlEVWlEX+/nsgRIssht2pRgwqp4iPjyc7O5u//e1vgHKhLigoAGDp0qW8+uqrAGzatKnzmMbGRlJTU9EbzXz4yWccP34cgLi4OJqbm3t/iC9BfcOaq/n1r39NY2Mj8+bNA+CSSy7hD3/4Q+csky+//DI0XzRIBJpjuAd4X0o5DXjf97wnbcDNUso5wKXA74QQXSen/ERKOd/3M3R3HmGYk1IAqKksC7MloaVrxBCpON1OWl2tao5BpRsvvvgiTz31FLm5ucyZM4fXX38dgN/97nc8/PDDLF68mIqKChJ88vI33XQTeXl5LLzoal58ZSszZ84EwGazcfbZZzN37lx+8pOfnPoAX3PbmquvYtOmTVx33XWdb/385z/H5XKRk5PD3Llz+fnPfz5K33pkBLqVtBpY4Xv8HPARcHfXBVLKr7o8LhdCVAMpQGTfcg6TuNRxADRVV4TZktCiTYj8iEGVwzi9ycrKoqioqPO5P2cA8M9//rPX+vHjx7Njxw6EEGzatImFCxcCkJyczPbt26GxTFFNTc/tPOavf/1rt3MUFRWBU6m+SktPx+3u3hBnNps7t5WigUAdQ5qUsgJASlkhhEgdaLEQYjFgALpOtPmlEOI+fBGHlLLPZgAhxK3ArQCZmZkBmh18ktImAOC014TZktASDVtJqhyGynDYvXs3d9xxB1JKrFYrTz/9dPcFGp2ileT1gmaATZYI0EkKFoN+AyHEe8C4Pt762XA+SAiRDrwArJWyU5Hqp0AlirN4AiXaeKCv46WUT/jWsHDhwogbOpwxYRJOwNUwzDGAUUZUOQY1YlAZAsuXL+/MN/RJ1yY3jaH/daeTY5BSXtTfe0KIKiFEui9aSAeq+1kXD7wF3Cul3NHl3P59l3YhxDPA+r6OjwYyMjI5AtAUuXvvwUATGwN6fWQ7Bt9WkqqTpBIUunU/D8UxRH+5eqDJ563AWt/jtcDrPRcIIQzAa8DzUsq/9Xgv3fenAK4EinoeHy0YjUZaTQJtX9UKYwghhE8WI3IdoF9AT40YVILCUIX0/HIYY0BZNlDHsAG4WAhxGLjY9xwhxEIhxJO+NdcB5wLr+ihLfVEIsRfYCyQDDwVoT1hpMWswtLaF24yQo02IbL0ku8OORW/BqDUOvlhFZTC0w3QMY4CAvoWU0g5c2MfrecAtvsd/Af7Sz/EXBPL5kUaLWY+5bQyMMxyESFdYVXsYVILKkCMGz5hxDKpWUhBxmA3EODrCbUbIiXjH4FB1klQC4/HHH+f5558H4NnnXqC8sqbTMdxyyy3s37+/90HesSG5DapjCCrOGDMW5xAHekQxEe8Y1IhBJUBuu+02br75ZgCefe45yqvrwesC4Mknn2T27D7kVtStJJW+cFksxDkGFtkaC+h8jkFKGZEjHO0OO4vHLQ63GSoA/7gHKvcG95zj5sFlG/p9u6SkhEsvvZQlS5bw5ZdfMn36dJ5//nm2b9/O+vXrcbvdLFq0iMceewyj0cg999zD1q1b0el0rFy5ko0bN3L//fdjsVjIysoiLy+Pm+64B7PZzPadu7nsssvYuHEju3btori4mF//+tcgJc++9Hd2HzzOH/70NH/5y1/4v//7Pzo6OliyZAmPPvpoVIlrqhFDEPHGxWFyQVNz5FbsBANNQgLS5UI6Im9incvjoqmjSd1KOs05dOgQt956K4WFhcTHx/Pwww+zbt06Nm/e3DkT4bHHHqOuro7XXnuNffv2UVhYyL333tvtPGvWrGHhwoW8+PhvyP/gNcxmc7f3/v73vytPpIfNb/yL66+5igMHDrB582Y+//xz8vPz0Wq1vPjii6P59QNGjRiCiEhQNARPlBYzZ07U6gEOStcmN01MTJit6Y5aqhphDHBnH0omTpzI2WefDcA3v/lNHnzwQbKzs5k+fToAa9eu5ZFHHuGOO+7AZDJxyy238PWvf51Vq1b1fUKh65V8TklJYfLkyezYsYNp2RM5dPQ4Z5+9jEeeeYndu3ezaNEiABwOB6mpA4pCRByqYwgihkRFhrqq/Php4xj0XQabRAKdOklqxHBaM9QtTp1Ox86dO3n//ffZtGkTf/zjH/nggw96L9RqO0XyunL99dfz8ssvM3NKFlddej5Cq0dKydq1a/nv//7vQL9G2FC3koJIrE25K6ivLA+zJaFFF8GyGKochgpAaWmpIoAHvPTSS1x00UWUlJRw5MgRAF544QXOO+88WlpaaGxs5Gtf+xq/+93v+pzXEBcXR3OrE6RH0UzqwtVXX82WLVt46eW/cf0VK0Gj48ILL+SVV16huloRgqirq+uU7I4W1IghiFjTlLvnttqxnYCOZL0kNWJQAZg1axbPPfcc3/ve95g2bRq///3vWbp0Kddee21n8vm2226jrq6O1atX43Q6kVLy29/+tte51q1bx20/uhuzXsv2L3Z2ey8xMZHZs2ezf18Ri8+YCxots2fP5qGHHmLlypV4vV70ej2PPPIIkyZFzwAv1TEEkdSMiQC0N9SG2ZLQovHp1UfiTAY1YlAB0Gg0PP74491eu/DCC3sNyElPT2fnzu4Xe4D777+/8/E111zDNV+7EOqLwaDno48+6rb2zTffhOYqaC7vLFe9/vrruf7664lW1K2kIDJx4mQAPA31YbYktETyVlKdsw6T1oRZZx58sYrKUBms+9nrBgSIsXFJHRvfIkJITLTRrgNNc1O4TQkpwmBAExMTkY7B39wWif0VKqNDz0E9QWEojmGMCOiB6hiCTrNZg76lJdxmhJxI7X5W5TBUQsJQHcMYQXUMQabVrMXUFnmNX8FGa7XijkTH4LSTZFbnMKgEGb8GkuoYVEZCq9mA2dHndNIxhdZqxRuBMxnUiEElJAihXPj7dQxjR1kVVMcQdBxmE7GO00FIL/JmMni8HhraG9SKJJXQMKBjcJ+a2zAGCMgxCCGShBDvCiEO+/5M7Gedp8uQnq1dXs8WQnzhO36zb9pbVNMRG0Oco3eH5FgjEnMMDe0NeKVXHempEhr6cQxbXvs7+w8dHlRyu6uUd3/k5+fz9ttvB2RmMAg0YrgHeF9KOQ143/e8LxxSyvm+nyu6vP4/wG99x9cD3wnQnrDjtsRhcUpcLle4TQkpWqsVT1MT0hM5TrCzuU2NGFRCgUYHnu6Owe12s2XLFvZ/dWzQraSuUt79ESmOIdDYZzWwwvf4OeAj4O6hHOib83wBcGOX4+8HHgvQpvASH49GQkVFGZmZ2eG2JmRorVaQEk9TE7rEPgPFUaezuU3NMUQM/7PzfzhYdzCo55yZNJO7F/d/mSkpKWHVqlWdJasbN26kpaWFjz76iCVLlvDhhx/S0NDAU089xfLly/F4PNx999288847CCH47ne/y5133snu3bv58Y9/TEtLC8nJyTz7+1+SnmBgxYoVLFu2jM8//5yVK1ey9Y03+fjD93noj8/z6mtb+OCDD3jiiSfo6Ohg6tSpvPDCC8TExHRKea9fv54VK1b0smXJkiXcd999OBwOPvvsM376059y7733sm3bNlJSUvB6vUyfPp0dO3aQnJwc1L/TngTqGNKklBUAUsoKIUR/EoImIUQe4AY2SCm3ADagQUrpd8FlwPj+PkgIcStwK0BmZmaAZocOnVW5KJWXlYx9xwB4GxshUhyDGjGoDILb7Wbnzp28/fbb/OIXv+C9997jiSeeoLi4mC+//BKdTkddXR0ul4s777yT119/nZSUFDZv3szPfrmRp/9HcUgNDQ18/PHHABw+uJ9Vy+ezZu1tYIzDarXy3e9+F4B7772Xp556ijvvvHNItjzwwAPk5eXxxz/+EYCDBw/y4osv8sMf/pD33nuP3NzckDsFGIJjEEK8B4zr462fDeNzMqWU5UKIycAHQoi9QF9dYLK/E0gpnwCeAFi4cGG/68KNyZYCgL2yLMyWhBatXxYjgvIMasQQeQx0Zx8Orr76agAWLFhASUkJAO+99x633XYbOp1yOUxKSqKoqIiioiIuvvhiADweD+mpvv9XUvaQu/BdjnxbSUVFRdx77700NDTQ0tLCJZdcMmRbevLtb3+b1atX88Mf/pCnn36ab33rWyP85sNjUMcgpbyov/eEEFVCiHRftJAOVPdzjnLfn8eEEB8BZwCvAlYhhM4XNUwAol6WNC5F8aFNNRVhtiS0+COGSOplsDvt6DQ64g3x4TZFJYzodDq83lMqqE6ns/Ox0WgEQKvV4nYrmxV9TSKUUjJnzpxOhVYAHPVQXwJIYmNjuyz2fZbPMaxbt44tW7aQm5vLs88+20tbaSBbejJx4kTS0tL44IMP+OKLL0Zt4E+gyeetwFrf47XA6z0XCCEShRBG3+Nk4Gxgv5RSAh8CawY6PtpITp8AgNNeE2ZLQkskKqzWOepIMiWpchinOWlpaVRXV2O322lvb1dE7gZg5cqVPP74450X57q6OmbMmEFNTU2nY3C5XOw7eFg5QHbfsIiLjaW5tbWzKqm5uZn09HRcLtewL+RxcXE0Nzd3e+2WW27hm9/8Jtddd92ojQcN1DFsAC4WQhwGLvY9RwixUAjxpG/NLCBPCFGA4gg2SCn3+967G/ixEOIISs7hqQDtCTsZ47MAcDXYw2tIiIlEx2B3qs1tKqDX67nvvvtYsmQJq1atYubMmQOuv+WWW8jMzCQnJ4fc3Fz++te/YjAYeOWVV7j77rvJzc1l/vz5bPtil++I7o7hhqtX8b+PvcAZZy7g6NGjPPjggyxZsoSLL7540M/uyfnnn8/+/fuZP38+mzdvBuCKK66gpaVl1LaRAISUEbtd3y8LFy6UeXl54TajT1wuF1/l5PD5ebO59fFXw21OyJBeLwfnzsP2vVtJveuucJsDwHVvXIfNbOOxi6K7sC3aOXDgALNmzQq3GcHH44KqIkiYALEpp16vL4GOVkibE5KPzcvL40c/+hGffvrpsI7r699BCLFbSrlwsGPVzucgo9fraTEJtC3Ngy+OYoRGgzY+Xo0YVE4f/A1sPXoZQqmTtGHDBq655ppRHxOqOoYQ0GLSYGxtC7cZISeSup+llNQ569RSVZXQITQgtL27nz2hcwz33HMPx48f55xzzgnJ+ftDdQwhoNWsx9zmHHxhlBNJjqGpowm3161GDCqhpS9ZjDGmrAqqYwgJbWYjMY6xLYkBfscQGQqr/uY2VXJbJaT06RjGlrIqqI4hJLTHmrGcFgqrkRMxqM1tKqOCtodj8HoAr+oYVAanIzYWi9M7+MIoR5uQgKcxsiIGNcegElJ6Rgxen4jkIMqq0YbqGEKAjI/H5IKGxvpwmxJStIlWZFsb3o6OcJuiRgwq3bBYLACUl5ezZo3SQ9tTuXTr1q1s2LBhwPN0PR445Rj8Zf5+J6FGDCqDIeIVUbmy0uIwWxJaOpvc6sO/nWR32NEIDVajNdymqEQQGRkZvPLKK0Bvx3DFFVdwzz39TQrofTzQZfazL1IYo45hbH2bCMGQqNy1VleegHlnhtma4dHe1ooxJnbwhXTvftan9SesOzrUOetINCaiHWMhfbRT+atf0X4guLLbxlkzGfef/zmktX4J7j179vSStHY4HJ1KpkePHuWmm27C4/Fw2WWX8fDDD9PS0tJNwtvpdPL97/+QvLxd6IyxPPzb33L+klye3byVvMMV/PHRxwFYtWoV69evZ/ny5XznO98hLy8PIQTf/va3+dGPfhTUv4tQoUYMISDWplwk6yujSxPweGE+j9/6b1QdOzKk9ZEki2F32tWKJJV+MRgMPPDAA1x//fXk5+f3UEeFu+66i7vuuotdu3aRkZHR5zkeeeQREBr2vv8yLz3/FGvXrsXZ1qK8KXpfSvPz8zl58iRFRUXs3bt3VCUtAkWNGEKANU35j9VWWxVmS4bHuKnTEBoNX/7zTS79wQ8HXd/pGBrD7xjqHHVqfiECGeqdfbjZvn07W7ZsAeDGG29k/fr1vdZ89tln3HmbMmdh5rQpTJo0ia+++sr3bm/hxsmTJ3Ps2DHuvPNOvv71r7Ny5cqQ2R9s1IghBKRmTASgvaE2zJYMD2NMLLPPvYCD2z6mrWnwaqNImslgd9rViiSVkCKlPFV95M8teD3o9Aa8XTTn/DLfiYmJFBQUsGLFCh555BFuueWW0TZ5xKiOIQRMnDgZAE9D9FUlnXHpKjwuF3vff2fQtae2ksJbsiqlxO5QdZJUBqYvSWs/S5cu5dVXFdHLTZs29bnm3HPP5cWXFMXTrw4dorS0lBlTMsmaNJH8/Hy8Xi8nTpxg586dANTW1uL1ernmmmt48MEH2bNnTwi+VWhQHUMISEy00a4DTXNfQ+oiG9uETDLn5lLw7j/wejwDrtWYzQijMewRQ5u7DafHqUYMKgPSl6S1n9/97nc8/PDDLF68mIqKChJ80XBXfvCDH+Dxepl34XVcv+57PPvssxj1Os5eupjs7GzmzZvH+vXrOfNMpeDk5MmTrFixgvnz57Nu3bpRF8ILBDXHECKazRr0LS3hNmNEnHHp5by+8SGO5O1g+pKzB1wbCd3Pag+DSk9afL97WVlZFBUVAcrIzl27dnVbt27dOgDGjx/Pjh07EEKwadMmFi5c2Ot4k8nEs88+C1X7QB8LSVlQfQChM/Y7kCeaooSuqI4hRLSatZjaHOE2Y0RMXrCI+JQ0vvznG0NzDGHufq5z1gFq17PKyNm9ezd33HEHUkqsVitPP/10/4u7dj973aAZWnl3NBGQYxBCJAGbgSygBLhOSlnfY835wG+7vDQTuEFKuUUI8SxwHuC/sqyTUuYHYlOk0Go2YHa0h9uMEaHRaJm/8mt88uIz1BwvJmVSdr9rtQkJERMxJJnUclWVkbF8+XIKCgqGtlijU4b2SDkmlVUh8BzDPcD7UsppwPu+592QUn4opZwvpZwPXAC0Af/qsuQn/vfHilMAcJhNxEaxkN7cC1aiMxj58p2B5+VGxFaSU91KUhlFOmUxPKeejzECdQyrged8j58Drhxk/RrgH1LKMT/FpiM2hjjHwMnbSMZsiWPWOedx4NOPcAwwjS4iHINDldxWGUX8CquesSmHAYE7hjQpZQWA78/BdBEFeandAAAU4UlEQVRuAF7q8dovhRCFQojfCiGM/R0ohLhVCJEnhMirqakJzOpRwG2Jw+KUuFzRO5fhjEsvx93RTtGH7/a7xp9jCOfscLvTToIxAb1GHzYbVE4jNDpAgqejy/OxxaCOQQjxnhCiqI+f1cP5ICFEOjAP6Fog/1OUnMMiIAm4u7/jpZRPSCkXSikXpqSk9LcscoiPRyOhoqIs3JaMmJRJ2UyYNZf8d97C6+07+tFareB2421tHWXrTqH2MKiMKn5H4PZNaRyD+lyDOgYp5UVSyrl9/LwOVPku+P4Lf/UAp7oOeE1K2XkLLaWskArtwDPA4sC+TuSgsyoXqvKykvAaEiDzL1lFU00Vx/bk9fl+JHQ/q7OeVUaLX/3qV304hsEjhltuuYX9+/cPuObxxx/n+eefD9TEoBDoVtJWYK3v8Vrg9QHWfoMe20hdnIpAyU8UBWhPxGCyKVGNvTJ6IwaAqYuWYkmy8eU/3+jzfW1i+KW37U67WpGkMip0cwyuU45BSonX2/9wrieffJLZs2cPeO7bbruNm2++OVimBkSgm2MbgJeFEN8BSoFrAYQQC4HbpJS3+J5nAROBj3sc/6IQIgVFgSofuC1AeyKGuJRxADTVVITZksDQ6nTkXvw1Pt/8AvayE9gmTOz+fgQorKpbSZHLpy9/Re2J4DZ6Jk+0sPy66QOuef7559m4cSNCCHJycnjooYf49re/TU1NDSkpKTzzzDNkZmaybt064uPjycvLo7Kykl//+tesWbOGiooKrr/+epqamnC73Tz22GO89dZbOBwO5i9axpwp4/nlPXdy2U23c/5Fl7B9xw62bNnChg0b2LVrFw6HgzVr1vCLX/wCgBUrVrBx40YWLlyIxWLhrrvu4s0338RsNvP666+TlpbG/fffj8ViYf369axYsYIlS5bw4Ycf/v/27j04qjLN4/j3SdMhkISARuQSgYisBGIug0CEiAbGEhQRBXQsBmFDiTuYcWZ31NGyRGSlBgvHRUSlQEZ2R7zAwKyU4sAgt+UiEiYXICIDEpSBAQ1GDAm5kHf/OCdNOnSSJp3uk5DnU5VK9zknOb/upPvpc3teiouLWbZsGbfeeiulpaVMmzaNgwcPkpCQQGFhIa+//rrngrzmEtAWgzGmyBgzyhjTz/5+xp6eXVMU7PuFxpiexpjqOj8/0hhzk71r6ufGmNZ5qbAPsd3jADhf1PIPlDcmadSduNq1I3fDpaeuOl0Yyi+UU1JZoruSlMeBAweYO3cumzZtIi8vj1dffZWsrCwefvhh8vPzmTx5Mo8//rhn+ZMnT7J9+3Y++ugjz8A97777LnfeeSe5ubnk5eWRkpLCvHnz6NChA7k5f2PForlgqvnySCEPT51KTk4OvXv3Zu7cuWRnZ5Ofn8/WrVvJz8+/JN+5c+dIS0sjLy+PESNGsHTpUp+Po6qqis8//5wFCxZ4Cswbb7xBly5dyM/P57nnnmPv3r1BeAb1yueg6dGzD6VAZXGR01EC1jGmMzcOG8GBrZtI/9lU2nfs6Jl3sfW2M1c/azuMlq2xT/bBsGnTJiZOnEhsbCxgtcLYtWsXa9asAWDKlCk89dRTnuXHjx9PWFgYAwYM4NQpq1X+4MGDyczMpLKykvHjx5OSknJxBWEuaj5T947rQVpammfWypUrWbJkCVVVVZw8eZKCggKSkpK88oWHhzN27FgABg0axF//6vusv/vvv9+zTGFhIWC1/v7Vr34FQGJi4iW/u7loE70g6dGjF9UC+NG+ujVIHX0PlefLOLB1o9d0V6dOgHNbDJ7CoFsMymaMwTpsWb/a89u3v3iWfM1p1yNGjGDbtm307NmTKVOmXHpQ2GV9po6MvPgh6ejRo7z88st8+umn5Ofnc/fdd3tacNfmdrs963e5XFRV+b4QtiZX7WVCdVq4FoYgcbvdlEQIrgYuDmtNuvXtR/d+N5K7/iNMrYNs0q4dYdHRjhUGT58k3WJQtlGjRrFy5UqKiqwPDWfOnGHYsGGedtorVqwgPT29wd9x7NgxunbtyiOPPML06dM9zfDcbrd1bZLnFNWLBebs2bNERkYSExPDqVOn+OSTT5r9saWnp7Ny5UoACgoK2LdvX7OvA3RXUlCVRITR/tyVc5F36uh7WPfayxTm5xCfMsgz3cmrn2vaYehVz6rGwIEDefbZZ7nttttwuVykpqaycOFCMjMzmT9/vufgc0O2bNnC/PnzcbvdREVFebYYZsyYQVJSEj8Z2I+5Tz7qNXBbcnIyqampDBw4kOuvv57hwxtuQNkUM2fOZOrUqSQlJZGamkpSUpLPFuGBEievWG2qm2++2WRn+z6vviVZOzKVSreLCetbflZ/XKiqZOljmXSN78v9T8/2TD866QFcMTH0esv3QbRgWpq/lIU5C9kzeQ8R7SJCvn51qS+++IKEhASnYwTX98eg7AxEd4Po7iFb7YULF6isrCQiIoIjR44watQoDh06RHh4+CXL+vo7iMheY0yjpzDpFkMQlXZoT8yPrbP1ti+udm6SfjqaXavf5/t/nqBLN2tsa1fnzlz43pnR6orOFxHpjtSioEKr5lqGELfDKC0tJSMjg8rKSowxvPnmmz6LQqD0GEMQlUd2IKoVd1j1JemnYwgLCyN3/ceeaa6YGEfPStLjCyrkao4xhLgwREdHk52dTV5eHvn5+YwZMyYo69HCEEQVkZFEna//asjWKKrLVfxLWjr7N/+VivPW1pDTxxj0jCQVcg5tMYSKFoYgMp06EVEJxT84s5slWFJHj6WirJSCbZsBqzBU//gjpp7T7oLpTNkZ3WJQoefuCBIG7eptCN2qaWEIIunUBYDjXx91OEnz6t6vP13j+1qnrhrj6EVu2idJOSK8I3RPBlfz799vCbQwBFH4VdYn2VMnvnY4SfMSEVJH30PR8a/5en+eY20xKqsrKS4v1l1JSjUzLQxBFHX1tQAUn/qHw0maX/9hI+gQ3Ymcv3zk2BbD9+etXXS6K0nVNWzYsEaXWbBgAaWlwb3OKDc3l3Xr1nnur127lnnz5gV1nc1BC0MQdb62JwAlRaccTtL82oWHc9OoO/lq7+ecs8e+DfUWg7bDUPXZuXNno8s0pTBcuHB5w/XWLQzjxo3zNOprya7MQ+otxLU9egFQcab1N9LzJfmOu9izdjUF+3PoRujHZKi56lkLQ8u1efkSTh/7qll/Z9fe15MxbUaDy0RFRVFSUsKWLVuYPXs2sbGx7N+/n0GDBvHOO+/w2muvceLECTIyMoiNjWXz5s1s2LCB559/nvLycvr27cvbb79NVFQUffr0ITMzkw0bNpCVlcXgwYN57LHH+Pbbb+nYsSNLly6lf//+rFq1ihdeeAGXy0VMTAwbN25k1qxZlJWVsX37dp555hnKysrIzs5m0aJF9bb8rq6uJisri61btxIfH091dTWZmZlMnDixWZ/HhmhhCKK4XvGcBMzZK+uspBqdYq/hhsFpHPh8J9eIOLfFoLuSVANycnI4cOAAPXr0YPjw4ezYsYPHH3+cV155hc2bNxMbG8t3333Hiy++yMaNG4mMjOSll17ilVdeYdasWQBERESwfft2wOrFtHjxYvr168fu3buZOXMmmzZtYs6cOaxfv56ePXtSXFxMeHg4c+bM8RQCgOXLl3tlq2n5ffDgQcaNG8fEiRNZs2YNhYWF7Nu3j9OnT5OQkEBmZmZInzMtDEHUOaYLR90gZ886HSVoUkffw9937+TE1TF0DXFhqGmgp2cltVyNfbIPhSFDhhAXZ42PkpKSQmFh4SVN9D777DMKCgo8/Y0qKiq45ZZbPPMffPBBAEpKSti5cyeTJk3yzCsvLwdg+PDhTJs2jQceeMDTMrsxvlp+b9++nUmTJhEWFka3bt3IyMho4iNvuoAKg4hMAmYDCcAQY4zPpkAiMhp4FXABbxlj5tnT44H3gauAvwFTjDEVgWRqaUoiwgg/d87pGEETl5BIbK8+HKu8QKIDWwztXe2JdEeGdL2qdandVru+NtfGGO644w7ee++9S+YBREZa/2PV1dV07tyZ3NzcS5ZZvHgxu3fv5uOPPyYlJcXnMg1lq+lb1xL61wV68Hk/cD+wrb4FRMQFvA6MAQYAD4lIzeCnLwH/ZYzpB3wPTA8wT4tT0qEd7c9dOf2S6rJOXR3LWbeLf377z5Cuu+i81Q6jsd77SvkSHR3Njz9abfHT0tLYsWMHhw8fBqyeRIcOHbrkZzp16kR8fDyrVq0CrDfxvLw8AI4cOcLQoUOZM2cOsbGxfPPNN17r8Fd6ejqrV6+murqaU6dOsWXLlgAeZdMEOrTnF8aYLxtZbAhw2Bjzlb018D5wr1iv5pHAn+zl/hsYH0ielqi0g5uOZeVOxwiqhPTbcSP8/YfvQrreojJth6GabsaMGYwZM4aMjAyuueYali9fzkMPPURSUhJpaWkcPHjQ58+tWLGCZcuWkZyczMCBA/nwww8BePLJJ7nppptITExkxIgRJCcnk5GRQUFBASkpKXzwwQd+5ZowYQJxcXEkJiby6KOPMnTo0KC01m5Is7TdFpEtwBO+diWJyERgdM0Y0CIyBRiKtQvqM2PMDfb064BPjDGJ9axjBjADoFevXoOOHTsWcO5QWPLryUhJCY+89aHTUYJq09O/oaqigjt+vzBkn+AX5SzCJS5+kfKLkKxP+adNtN0OspKSEqKioigqKmLIkCHs2LGDbt26XdbvCGrbbRHZCPhK9Kwxxp93O1/vEqaB6T4ZY5YAS8Aaj8GP9bYIMxascDpCSIyc9/uQrzMrNSvk61QqFMaOHUtxcTEVFRU899xzl10UAtVoYTDG/DTAdRwHrqt1Pw44AXwHdBaRdsaYqlrTlVKqTXPiuEJtobjyeQ/QT0TiRSQc+Bmw1lj7sDYDNVdtTAWu7P0tSrURLeHMmrYs0Oc/oMIgIveJyHHgFuBjEVlvT+8hIuvsgFVAFrAe+AJYaYw5YP+K3wL/ISKHgauBZYHkUUo5LyIigqKiIi0ODjHGUFRURERE00c11DGflVLNqrKykuPHj3P+/Hmno7RZERERxMXF4Xa7vabrmM9KKUe43W7i4+OdjqECoN1VlVJKedHCoJRSyosWBqWUUl5a5cFnEfkWaM5Ln2OxrqtobTR3aGnu0NLcza+3MeaaxhZqlYWhuYlItj9H6lsazR1amju0NLdzdFeSUkopL1oYlFJKedHCYFnidIAm0tyhpblDS3M7RI8xKKWU8qJbDEoppbxoYVBKKeWlTRUGERktIl+KyGERedrH/F4isllEckQkX0TuciJnXX7k7i0in9qZt4hInBM562T6g4icFpH99cwXEVloP6Z8EflJqDP64kfu/iKyS0TKReSJUOerjx+5J9vPc76I7BSR5FBn9MWP3PfamXNFJFtE0kOd0ZfGctdabrCIXLBHsmw9jDFt4gtwAUeA64FwIA8YUGeZJcAv7NsDgMJWknsVMNW+PRL4YwvIPQL4CbC/nvl3AZ9gjeSXBux2OrOfubsCg4G5WMPZOp7Zz9zDgC727TGt6PmO4uKx0CTgoNOZ/cltL+MCNgHrgIlOZ76cr7a0xTAEOGyM+coYUwG8D9xbZxkDdLJvx9AyRpTzJ/cA4FP79mYf80POGLMNONPAIvcC/2Msn2GN5tc9NOnq11huY8xpY8weoDJ0qRrnR+6dxpjv7bufYY2Y6Dg/cpcY+10WiKSB4X9DyY//b4BfAquB08FP1LzaUmHoCXxT6/5xe1pts4Gf24MPrcP6wzrNn9x5wAT79n1AtIhcHYJsgfDncangmI61tdYq2AOCHQQ+BjKdzuMPEemJ9Vpc7HSWpmhLhUF8TKv76eMhYLkxJg5rV8cfRcTp58if3E8At4lIDnAb8A+gKtjBAuTP41LNTEQysArDb53O4i9jzJ+NMf2B8cB/Op3HTwuA3xpjLjgdpCna0kA9x4Hrat2P49JdRdOB0QDGmF0iEoHVEMvJTcFGcxtjTgD3A4hIFDDBGPNDyBI2jT9/D9WMRCQJeAsYY4wpcjrP5TLGbBORviISa4xpqU3qatwMvC8iYL2H3CUiVcaY/3U2ln+c/jQcSnuAfiISLyLhwM+AtXWW+RoYBSAiCUAE8G1IU16q0dwiEltry+YZ4A8hztgUa4GH7bOT0oAfjDEnnQ51pRKRXsAaYIox5pDTefwlIjeI/e5qn7kWDrT4omaMiTfG9DHG9AH+BMxsLUUB2tAWgzGmSkSygPVYZwv8wRhzQETmANnGmLXAb4ClIvLvWLs1ptU68OUIP3PfDvxORAywDXjMscA2EXkPK1esfczmecANYIxZjHUM5y7gMFAK/KszSb01lltEugHZWCcpVIvIr7HOEjvrUGTAr+d7FnA18Ib9PltlWkAHUD9yT8D6AFEJlAEPOv2aBL9yt2raEkMppZSXtrQrSSmllB+0MCillPKihUEppZQXLQxKKaW8aGFQSqkWzt+mffayATfV1MKg1GUSkdkNdVYVkfEiMiCUmdQVbzn2xbd+eBmrD1kSMAf43eWuTAuDUs1vPFZjQ6Waha+mffZV4H8Rkb0i8n8i0t+eFXBTTS0MSvlBRJ61x8TYCNxoT3tERPaISJ6IrBaRjiIyDBgHzLfHEOjbwAtYqUAsAX5pjBmE1S/tDXt6wE0128yVz0o1lYgMwmpFkor1mvkbsBdYY4xZai/zIjDdGPOaiKwFPjLG/Mme9ynwb8aYv4vIUKwX8EgHHoq6Qtg90YYBq+wr2QHa29+fABaJyDSsTgiX3VRTC4NSjbsV+LMxphTAfuMHSLQLQmesAWXW1/3BRl7ASjVVGFBsjEmpO6M5mmrqriSl/OOrd8xyIMsYcxPwAlbTxbo8L+BaXwlBzKnaALs311ERmQSeoXKT7dsBN9XUwqBU47YB94lIBxGJBu6xp0cDJ0XEDUyutfyP9rwGX8BK+ctu2rcLuFFEjovIdKz/uekikgcc4OJB5tuBL0XkEHAt1jC0l7c+baKnVONE5FngYeAY1lgSBcA54Cl72j4g2hgzTUSGA0uBcmAiUA28CXTH6sD5vjFmTsgfhFJ+0sKglFLKi+5KUkop5UULg1JKKS9aGJRSSnnRwqCUUsqLFgallFJetDAopZTyooVBKaWUl/8HtnYJcJ9a4ssAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbb28d23f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_FRA = pd.DataFrame()\n",
    "\n",
    "for factor, data in unixt_factor_data.items():\n",
    "    ls_FRA[factor] = al.performance.factor_rank_autocorrelation(data)\n",
    "\n",
    "ls_FRA.plot(title=\"Factor Rank Autocorrelation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sharpe Ratio of the Alphas\n",
    "The last analysis we'll do on the factors will be sharpe ratio. Let's see what the sharpe ratio for the factors are. Generally, a Sharpe Ratio of near 1.0 or higher is an acceptable single alpha for this universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative       -0.24000000\n",
       "positive       -1.33000000\n",
       "uncertainty     0.82000000\n",
       "litigious       1.34000000\n",
       "constraining   -2.99000000\n",
       "interesting    -2.47000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_annualization_factor = np.sqrt(252)\n",
    "\n",
    "(daily_annualization_factor * ls_factor_returns.mean() / ls_factor_returns.std()).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! You've successfully done sentiment analysis on 10-ks!\n",
    "## Submission\n",
    "Now that you're done with the project, it's time to submit it. Click the submit button in the bottom right. One of our reviewers will give you feedback on your project with a pass or not passed grade. You can continue to the next section while you wait for feedback."
   ]
  }
 ],
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